feat(benchmarks): add multi tlu benchmark

benchmarks/multi_table_lookup.py

@@ -0,0 +1,60 @@
+# bench: Unit Target: Multi Table Lookup
+
+import math
+
+import numpy as np
+from common import BENCHMARK_CONFIGURATION
+
+import concrete.numpy as hnp
+
+
+def main():
+    input_bits = 3
+
+    square_table = hnp.LookupTable([i ** 2 for i in range(2 ** input_bits)])
+    sqrt_table = hnp.LookupTable([int(math.sqrt(i)) for i in range(2 ** input_bits)])
+    multi_table = hnp.MultiLookupTable(
+        [
+            [square_table, sqrt_table],
+            [square_table, sqrt_table],
+            [square_table, sqrt_table],
+        ]
+    )
+
+    def function_to_compile(x):
+        return multi_table[x]
+
+    x = hnp.EncryptedTensor(hnp.UnsignedInteger(input_bits), shape=(3, 2))
+
+    # bench: Measure: Compilation Time (ms)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        [(np.random.randint(0, 2 ** input_bits, size=(3, 2)),) for _ in range(32)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
+    # bench: Measure: End
+
+    inputs = []
+    labels = []
+    for _ in range(50):
+        sample_x = np.random.randint(0, 2 ** input_bits, size=(3, 2))
+
+        inputs.append([sample_x])
+        labels.append(function_to_compile(*inputs[-1]))
+
+    correct = 0
+    for input_i, label_i in zip(inputs, labels):
+        # bench: Measure: Evaluation Time (ms)
+        result_i = engine.run(*input_i)
+        # bench: Measure: End
+
+        if result_i == label_i:
+            correct += 1
+
+    # bench: Measure: Accuracy (%) = (correct / len(inputs)) * 100
+    # bench: Alert: Accuracy (%) != 100
+
+
+if __name__ == "__main__":
+    main()