feat: create a method in the new interface to compile using an inputset in one go

concrete/numpy/np_fhe_compiler.py

@@ -249,6 +249,26 @@ class NPFHECompiler:
                     dtype.bit_width = 128
                     dtype.is_signed = True
 
+    def compile_on_inputset(
+        self,
+        inputset: Union[Iterable[Any], Iterable[Tuple[Any, ...]]],
+        show_mlir: bool = False,
+    ) -> FHECircuit:
+        """Compile the function on an inputset and get resulting FHECircuit.
+
+        Args:
+            inputset (Union[Iterable[Any], Iterable[Tuple[Any, ...]]]):
+                The inputset on which the function is evaluated.
+            show_mlir (bool, optional, defaults to False):
+                The flag to enable printing the MLIR that is being compiled for debugging purposes.
+
+        Returns:
+            FHECircuit: the compiled FHECircuit
+        """
+
+        self.eval_on_inputset(inputset)
+        return self.get_compiled_fhe_circuit(show_mlir)
+
     def get_compiled_fhe_circuit(self, show_mlir: bool = False) -> FHECircuit:
         """Return a compiled FHECircuit if the instance was evaluated on an inputset.
 

concrete/quantization/quantized_module.py

@@ -121,8 +121,8 @@ class QuantizedModule:
             compilation_configuration,
             compilation_artifacts,
         )
-        compiler.eval_on_inputset((numpy.expand_dims(arr, 0) for arr in self.q_input.qvalues))
-
-        self.forward_fhe = compiler.get_compiled_fhe_circuit(show_mlir)
+        self.forward_fhe = compiler.compile_on_inputset(
+            (numpy.expand_dims(arr, 0) for arr in self.q_input.qvalues), show_mlir
+        )
 
         return self.forward_fhe

docs/dev/explanation/compilation.md

@@ -21,8 +21,8 @@ def f(x, y):
 compiler = hnp.NPFHECompiler(f, {"x": "encrypted", "y": "encrypted"})
 
 # Compile an FHE Circuit using an inputset
-compiler.eval_on_inputset([(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1), (3, 0), (3, 1)])
-circuit = compiler.get_compiled_fhe_circuit()
+inputset = [(0, 0), (0, 1), (1, 0), (1, 1), (2, 0), (2, 1), (3, 0), (3, 1)]
+circuit = compiler.compile_on_inputset(inputset)
 
 # Make homomorphic inference
 circuit.run(1, 0)

docs/user/basics/compiling_and_executing.md

@@ -44,6 +44,9 @@ Finally, we can compile our function to its homomorphic equivalent.
 compiler = hnp.NPFHECompiler(
     f, {"x": x, "y": y},
 )
+circuit = compiler.compile_on_inputset(inputset)
+
+# If you want, you can separate tracing and compilation steps like so:
 
 # You can either evaluate in one go:
 compiler.eval_on_inputset(inputset)

docs/user/howto/reduce_needed_precision.md

@@ -15,8 +15,7 @@ def f(x):
     return 42 * x
 
 compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
-compiler.eval_on_inputset(range(2 ** 3))
-compiler.get_compiled_fhe_circuit()
+circuit = compiler.compile_on_inputset(range(2 ** 3))
 ```
 
 results in

docs/user/tutorial/compilation_artifacts.md

@@ -125,7 +125,7 @@ def f(x):
 artifacts = hnp.CompilationArtifacts(pathlib.Path("/tmp/custom/export/path"))
 
 compiler = hnp.NPFHECompiler(f, {"x": "encrypted"}, compilation_artifacts=artifacts)
-compiler.eval_on_inputset(range(2 ** 3))
+compiler.compile_on_inputset(range(2 ** 3))
 
 artifacts.export()
 ```

docs/user/tutorial/indexing.md

@@ -18,8 +18,7 @@ def f(x):
 inputset = [np.random.randint(0, 2 ** 3, size=(3,), dtype=np.uint8) for _ in range(10)]
 
 compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
-compiler.eval_on_inputset(inputset)
-circuit = compiler.get_compiled_fhe_circuit()
+circuit = compiler.compile_on_inputset(inputset)
 
 test_input = np.array([4, 2, 6], dtype=np.uint8)
 expected_output = 2
@@ -39,8 +38,7 @@ def f(x):
 inputset = [np.random.randint(0, 2 ** 3, size=(3,), dtype=np.uint8) for _ in range(10)]
 
 compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
-compiler.eval_on_inputset(inputset)
-circuit = compiler.get_compiled_fhe_circuit()
+circuit = compiler.compile_on_inputset(inputset)
 
 test_input = np.array([4, 2, 6], dtype=np.uint8)
 expected_output = 6
@@ -60,8 +58,7 @@ def f(x):
 inputset = [np.random.randint(0, 2 ** 3, size=(3, 2), dtype=np.uint8) for _ in range(10)]
 
 compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
-compiler.eval_on_inputset(inputset)
-circuit = compiler.get_compiled_fhe_circuit()
+circuit = compiler.compile_on_inputset(inputset)
 
 test_input = np.array([[4, 2], [1, 5], [7, 6]], dtype=np.uint8)
 expected_output = 6
@@ -81,8 +78,7 @@ def f(x):
 inputset = [np.random.randint(0, 2 ** 3, size=(5,), dtype=np.uint8) for _ in range(10)]
 
 compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
-compiler.eval_on_inputset(inputset)
-circuit = compiler.get_compiled_fhe_circuit()
+circuit = compiler.compile_on_inputset(inputset)
 
 test_input = np.array([4, 2, 6, 1, 7], dtype=np.uint8)
 expected_output = np.array([2, 6, 1], dtype=np.uint8)

docs/user/tutorial/working_with_floating_points.md

@@ -13,9 +13,7 @@ def f(x):
 
 # Compiling with x encrypted
 compiler = hnp.NPFHECompiler(f, {"x": "encrypted"})
-compiler.eval_on_inputset(range(64))
-
-circuit = compiler.get_compiled_fhe_circuit()
+circuit = compiler.compile_on_inputset(range(64))
 
 assert circuit.run(3) == f(3)
 assert circuit.run(0) == f(0)