chore: seed torch as much as possible

closes #877

tests/conftest.py

@@ -1,6 +1,7 @@
 """PyTest configuration file"""
 import json
 import operator
+import random
 import re
 from pathlib import Path
 from typing import Callable, Dict, Type
@@ -8,6 +9,7 @@ from typing import Callable, Dict, Type
 import networkx as nx
 import networkx.algorithms.isomorphism as iso
 import pytest
+import torch
 
 from concrete.common.compilation import CompilationConfiguration
 from concrete.common.representation.intermediate import (
@@ -276,3 +278,18 @@ REMOVE_COLOR_CODES_RE = re.compile(r"\x1b[^m]*m")
 def remove_color_codes():
     """Return the re object to remove color codes"""
     return lambda x: REMOVE_COLOR_CODES_RE.sub("", x)
+
+
+def function_to_seed_torch():
+    """Function to seed torch"""
+
+    # Seed torch with something which is seed by pytest-randomly
+    torch.manual_seed(random.randint(0, 2 ** 64 - 1))
+    torch.use_deterministic_algorithms(True)
+
+
+@pytest.fixture
+def seed_torch():
+    """Fixture to seed torch"""
+
+    return function_to_seed_torch

tests/quantization/test_quantized_module.py

@@ -80,12 +80,14 @@ N_BITS_ATOL_TUPLE_LIST = [
         pytest.param(FC, (100, 32 * 32 * 3)),
     ],
 )
-def test_quantized_linear(model, input_shape, n_bits, atol):
+def test_quantized_linear(model, input_shape, n_bits, atol, seed_torch):
     """Test the quantized module with a post-training static quantization.
 
     With n_bits>>0 we expect the results of the quantized module
     to be the same as the standard module.
     """
+    # Seed torch
+    seed_torch()
     # Define the torch model
     torch_fc_model = model()
     # Create random input

tests/torch/test_torch_to_numpy.py

@@ -66,9 +66,11 @@ class FC(nn.Module):
         pytest.param(FC, (100, 32 * 32 * 3)),
     ],
 )
-def test_torch_to_numpy(model, input_shape):
+def test_torch_to_numpy(model, input_shape, seed_torch):
     """Test the different model architecture from torch numpy."""
 
+    # Seed torch
+    seed_torch()
     # Define the torch model
     torch_fc_model = model()
     # Create random input
@@ -104,9 +106,10 @@ def test_torch_to_numpy(model, input_shape):
     "model, incompatible_layer",
     [pytest.param(CNN, "Conv2d")],
 )
-def test_raises(model, incompatible_layer):
+def test_raises(model, incompatible_layer, seed_torch):
     """Function to test incompatible layers."""
 
+    seed_torch()
     torch_incompatible_model = model()
     expected_errmsg = (
         f"The following module is currently not implemented: {incompatible_layer}. "