fix(benchmarks): treat warnings as errors in benchmarks

benchmarks/124_minus_x.py

@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,6 +18,7 @@ def main():
         function_to_compile,
         {"x": x},
         [(i,) for i in range(2 ** 3)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

benchmarks/124_minus_x_tensor.py

@@ -1,6 +1,7 @@
 # Target: 124 - x (Tensor)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(6), shape=(3,))
 
-    inputset = [
-        (np.array([36, 50, 24]),),
-        (np.array([41, 60, 51]),),
-        (np.array([25, 31, 24]),),
-        (np.array([34, 47, 27]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 6, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/common.py

@@ -0,0 +1,8 @@
+import concrete.numpy as hnp
+
+BENCHMARK_CONFIGURATION = hnp.CompilationConfiguration(
+    dump_artifacts_on_unexpected_failures=True,
+    enable_topological_optimizations=True,
+    check_every_input_in_inputset=True,
+    treat_warnings_as_errors=True,
+)

benchmarks/linear_regression.py

@@ -5,13 +5,16 @@
 # pylint: disable=C0301
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
 
 def main():
-    x = np.array([[130], [110], [100], [145], [160], [185], [200], [80], [50]], dtype=np.float32)
-    y = np.array([325, 295, 268, 400, 420, 500, 520, 220, 120], dtype=np.float32)
+    x = np.array(
+        [[69], [130], [110], [100], [145], [160], [185], [200], [80], [50]], dtype=np.float32
+    )
+    y = np.array([181, 325, 295, 268, 400, 420, 500, 520, 220, 120], dtype=np.float32)
 
     class Model:
         w = None
@@ -150,6 +153,7 @@ def main():
         function_to_compile,
         {"x_0": hnp.EncryptedScalar(hnp.UnsignedInteger(input_bits))},
         inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

benchmarks/logistic_regression.py

@@ -2,13 +2,40 @@
 
 import numpy as np
 import torch
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
 
 def main():
-    x = torch.tensor([[1, 1], [1, 2], [2, 1], [4, 1], [3, 2], [4, 2]]).float()
-    y = torch.tensor([[0], [0], [0], [1], [1], [1]]).float()
+    x = torch.tensor(
+        [
+            [1, 1],
+            [1, 1.5],
+            [1.5, 1.2],
+            [1, 2],
+            [2, 1],
+            [4, 1],
+            [4, 1.5],
+            [3.5, 1.8],
+            [3, 2],
+            [4, 2],
+        ]
+    ).float()
+    y = torch.tensor(
+        [
+            [0],
+            [0],
+            [0],
+            [0],
+            [0],
+            [1],
+            [1],
+            [1],
+            [1],
+            [1],
+        ]
+    ).float()
 
     class Model(torch.nn.Module):
         def __init__(self, n):
@@ -218,6 +245,7 @@ def main():
             "x_1": hnp.EncryptedScalar(hnp.UnsignedInteger(input_bits)),
         },
         inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

benchmarks/single_table_lookup.py

@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -21,6 +23,7 @@ def main():
         function_to_compile,
         {"x": x},
         [(i,) for i in range(2 ** input_bits)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

benchmarks/x_minus_1_2_3.py

@@ -1,6 +1,7 @@
 # Target: x - [1, 2, 3]
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 5]),),
-        (np.array([5, 7, 2]),),
-        (np.array([1, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 2, size=(3,)) + np.array([1, 2, 3]),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_minus_1_2_3_broadcasted.py

@@ -1,6 +1,7 @@
 # Target: x - [1, 2, 3] (Broadcasted)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -12,14 +13,16 @@ def main():
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
     inputset = [
-        (np.array([[4, 7, 7], [6, 2, 4]]),),
-        (np.array([[6, 2, 4], [1, 3, 1]]),),
-        (np.array([[6, 2, 4], [5, 7, 5]]),),
-        (np.array([[5, 7, 5], [4, 7, 7]]),),
+        (np.random.randint(0, 2 ** 2, size=(2, 3)) + np.array([1, 2, 3]),) for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_minus_24.py

@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -15,7 +17,8 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x},
-        [(i,) for i in range(2 ** 6)],
+        [(i,) for i in range(24, 2 ** 6)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

benchmarks/x_minus_24_tensor.py

@@ -1,6 +1,7 @@
 # Target: x - 24 (Tensor)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(6), shape=(3,))
 
-    inputset = [
-        (np.array([36, 50, 24]),),
-        (np.array([41, 60, 51]),),
-        (np.array([25, 31, 24]),),
-        (np.array([34, 47, 27]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 5, size=(3,)) + 24,) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_minus_y.py

@@ -3,6 +3,8 @@
 import itertools
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,7 +18,12 @@ def main():
     inputset = itertools.product(range(4, 8), range(0, 4))
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_minus_y_broadcasted_tensors.py

@@ -1,6 +1,7 @@
 # Target: x - y (Broadcasted Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -10,17 +11,20 @@ def main():
         return x - y
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
-    y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
+    y = hnp.EncryptedTensor(hnp.UnsignedInteger(2), shape=(2, 3))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([[5, 1, 3], [0, 0, 4]])),
-        (np.array([1, 3, 1]), np.array([[0, 3, 1], [1, 2, 1]])),
-        (np.array([5, 1, 2]), np.array([[5, 0, 2], [2, 1, 1]])),
-        (np.array([0, 7, 7]), np.array([[0, 5, 1], [0, 7, 2]])),
+        (np.random.randint(4, 8, size=(3,)), np.random.randint(0, 4, size=(2, 3)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_minus_y_tensor_and_scalar.py

@@ -3,6 +3,7 @@
 import random
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -12,17 +13,17 @@ def main():
         return x - y
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
-    y = hnp.EncryptedScalar(hnp.UnsignedInteger(3))
+    y = hnp.EncryptedScalar(hnp.UnsignedInteger(2))
 
-    inputset = [
-        (np.array([6, 2, 4]), 2),
-        (np.array([1, 3, 1]), 1),
-        (np.array([5, 4, 7]), 4),
-        (np.array([5, 7, 6]), 5),
-    ]
+    inputset = [(np.random.randint(4, 8, size=(3,)), random.randint(0, 3)) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_minus_y_tensors.py

@@ -1,6 +1,7 @@
 # Target: x - y (Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -10,17 +11,19 @@ def main():
         return x - y
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
-    y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
+    y = hnp.EncryptedTensor(hnp.UnsignedInteger(2), shape=(3,))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([4, 1, 2])),
-        (np.array([1, 3, 1]), np.array([1, 1, 0])),
-        (np.array([5, 1, 2]), np.array([4, 1, 1])),
-        (np.array([0, 7, 7]), np.array([0, 7, 0])),
+        (np.random.randint(4, 8, size=(3,)), np.random.randint(0, 4, size=(3,))) for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_plus_1_2_3.py

@@ -1,6 +1,7 @@
 # Target: x + [1, 2, 3]
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 1]),),
-        (np.array([5, 1, 2]),),
-        (np.array([0, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_plus_1_2_3_broadcasted.py

@@ -1,6 +1,7 @@
 # Target: x + [1, 2, 3] (Broadcasted)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
-    inputset = [
-        (np.array([[0, 7, 7], [6, 2, 4]]),),
-        (np.array([[6, 2, 4], [1, 3, 1]]),),
-        (np.array([[6, 2, 4], [5, 1, 2]]),),
-        (np.array([[5, 1, 2], [0, 7, 7]]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(2, 3)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_plus_42.py

@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -15,7 +17,8 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x},
-        [(6,), (1,), (5,), (2,)],
+        [(i,) for i in range(2 ** 3)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

benchmarks/x_plus_42_tensor.py

@@ -1,6 +1,7 @@
 # Target: x + 42 (Tensor)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 1]),),
-        (np.array([5, 1, 2]),),
-        (np.array([0, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_plus_y.py

@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,7 +18,8 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x, "y": y},
-        [(6, 1), (1, 4), (5, 3), (2, 0), (7, 7)],
+        [(random.randint(0, 7), random.randint(0, 7)) for _ in range(32)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

benchmarks/x_plus_y_broadcasted_tensors.py

@@ -1,6 +1,7 @@
 # Target: x + y (Broadcasted Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -13,14 +14,17 @@ def main():
     y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([[5, 1, 2], [0, 7, 7]])),
-        (np.array([1, 3, 1]), np.array([[0, 7, 7], [6, 2, 4]])),
-        (np.array([5, 1, 2]), np.array([[6, 2, 4], [1, 3, 1]])),
-        (np.array([0, 7, 7]), np.array([[1, 3, 1], [5, 1, 2]])),
+        (np.random.randint(0, 2 ** 3, size=(3,)), np.random.randint(0, 2 ** 3, size=(2, 3)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_plus_y_tensor_and_scalar.py

@@ -3,6 +3,7 @@
 import random
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -14,15 +15,15 @@ def main():
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
     y = hnp.EncryptedScalar(hnp.UnsignedInteger(3))
 
-    inputset = [
-        (np.array([6, 2, 4]), 4),
-        (np.array([1, 3, 1]), 1),
-        (np.array([5, 1, 2]), 2),
-        (np.array([0, 7, 7]), 5),
-    ]
+    inputset = [(np.random.randint(0, 8, size=(3,)), random.randint(0, 7)) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_plus_y_tensors.py

@@ -1,6 +1,7 @@
 # Target: x + y (Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -13,14 +14,17 @@ def main():
     y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([0, 7, 7])),
-        (np.array([1, 3, 1]), np.array([6, 2, 4])),
-        (np.array([5, 1, 2]), np.array([1, 3, 1])),
-        (np.array([0, 7, 7]), np.array([5, 1, 2])),
+        (np.random.randint(0, 2 ** 3, size=(3,)), np.random.randint(0, 2 ** 3, size=(3,)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_times_1_2_3.py

@@ -1,6 +1,7 @@
 # Target: x * [1, 2, 3]
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 1]),),
-        (np.array([5, 1, 2]),),
-        (np.array([0, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_times_1_2_3_broadcasted.py

@@ -1,6 +1,7 @@
 # Target: x * [1, 2, 3] (Broadcasted)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
-    inputset = [
-        (np.array([[0, 7, 7], [6, 2, 4]]),),
-        (np.array([[6, 2, 4], [1, 3, 1]]),),
-        (np.array([[6, 2, 4], [5, 1, 2]]),),
-        (np.array([[5, 1, 2], [0, 7, 7]]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(2, 3)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_times_7.py

@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,6 +18,7 @@ def main():
         function_to_compile,
         {"x": x},
         [(i,) for i in range(2 ** 4)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

benchmarks/x_times_7_tensor.py

@@ -1,6 +1,7 @@
 # Target: x * 7 (Tensor)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -11,15 +12,15 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [
-        (np.array([6, 2, 4]),),
-        (np.array([1, 3, 1]),),
-        (np.array([5, 1, 2]),),
-        (np.array([0, 7, 7]),),
-    ]
+    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_times_y.py

@@ -3,6 +3,8 @@
 import itertools
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -16,7 +18,12 @@ def main():
     inputset = itertools.product(range(4, 8), range(0, 4))
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_times_y_broadcasted_tensors.py

@@ -1,6 +1,7 @@
 # Target: x * y (Broadcasted Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -13,14 +14,17 @@ def main():
     y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([[5, 1, 2], [0, 7, 7]])),
-        (np.array([1, 3, 1]), np.array([[0, 7, 7], [6, 2, 4]])),
-        (np.array([5, 1, 2]), np.array([[6, 2, 4], [1, 3, 1]])),
-        (np.array([0, 7, 7]), np.array([[1, 3, 1], [5, 1, 2]])),
+        (np.random.randint(0, 2 ** 3, size=(3,)), np.random.randint(0, 2 ** 3, size=(2, 3)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_times_y_tensor_and_scalar.py

@@ -3,6 +3,7 @@
 import random
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -14,15 +15,15 @@ def main():
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
     y = hnp.EncryptedScalar(hnp.UnsignedInteger(3))
 
-    inputset = [
-        (np.array([6, 2, 4]), 4),
-        (np.array([1, 3, 1]), 1),
-        (np.array([5, 1, 2]), 2),
-        (np.array([0, 7, 7]), 5),
-    ]
+    inputset = [(np.random.randint(0, 8, size=(3,)), random.randint(0, 7)) for _ in range(32)]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_times_y_tensors.py

@@ -1,6 +1,7 @@
 # Target: x * y (Tensors)
 
 import numpy as np
+from common import BENCHMARK_CONFIGURATION
 
 import concrete.numpy as hnp
 
@@ -13,14 +14,17 @@ def main():
     y = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
     inputset = [
-        (np.array([6, 2, 4]), np.array([0, 7, 7])),
-        (np.array([1, 3, 1]), np.array([6, 2, 4])),
-        (np.array([5, 1, 2]), np.array([1, 3, 1])),
-        (np.array([0, 7, 7]), np.array([5, 1, 2])),
+        (np.random.randint(0, 2 ** 3, size=(3,)), np.random.randint(0, 2 ** 3, size=(3,)))
+        for _ in range(32)
     ]
 
     # Measure: Compilation Time (ms)
-    engine = hnp.compile_numpy_function(function_to_compile, {"x": x, "y": y}, inputset)
+    engine = hnp.compile_numpy_function(
+        function_to_compile,
+        {"x": x, "y": y},
+        inputset,
+        compilation_configuration=BENCHMARK_CONFIGURATION,
+    )
     # Measure: End
 
     inputs = []

benchmarks/x_to_the_power_of_2.py

@@ -2,6 +2,8 @@
 
 import random
 
+from common import BENCHMARK_CONFIGURATION
+
 import concrete.numpy as hnp
 
 
@@ -15,7 +17,8 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x},
-        [(6,), (1,), (5,), (2,)],
+        [(i,) for i in range(2 ** 3)],
+        compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # Measure: End
 

script/progress_tracker_utils/measure.py

@@ -261,7 +261,8 @@ def main():
     scripts = list(base.glob("*.py"))
 
     # Create a directory to store temporary scripts
-    os.makedirs(".benchmarks/scripts", exist_ok=True)
+    shutil.rmtree(".benchmarks/scripts", ignore_errors=True)
+    shutil.copytree(base, ".benchmarks/scripts")
 
     # Process each script under the base directory
     for path in scripts: