Mark tests slower than 1.5 seconds (#1633)

* mark tests slower than 1.5 seconds

* changelog

* black

* Update .github/CHANGELOG.md

Co-authored-by: Romain <rmoyard@gmail.com>

Co-authored-by: Romain <rmoyard@gmail.com>

.github/CHANGELOG.md

@@ -304,6 +304,10 @@
 
 <h3>Improvements</h3>
 
+* The slowest tests, more than 1.5 seconds, now have the pytest mark `slow`, and can be
+  selected or deselected during local execution of tests.
+  [(#1633)](https://github.com/PennyLaneAI/pennylane/pull/1633)
+
 * Hamiltonians are now natively supported on the `default.qubit` device if `shots=None`. 
   This makes VQE workflows a lot faster in some cases.
   [(#1551)](https://github.com/PennyLaneAI/pennylane/pull/1551)

tests/circuit_drawer/test_circuit_drawer.py

@@ -709,6 +709,7 @@ def test_wide_cv_circuit(self, wide_cv_qnode, drawn_wide_cv_qnode):
 
         assert output == drawn_wide_cv_qnode
 
+    @pytest.mark.slow
     def test_cv_circuit_with_values(
         self, parameterized_cv_qnode, drawn_parameterized_cv_qnode_with_values
     ):

tests/circuit_graph/test_qasm.py

@@ -700,6 +700,7 @@ def qnode():
         assert res == expected
 
 
+@pytest.mark.slow
 class TestQASMConformanceTests:
     """Conformance tests to ensure that the CircuitGraph
     serialized QASM conforms to the QASM standard as implemented

tests/gradients/test_finite_difference.py

@@ -504,6 +504,7 @@ def cost_fn(x):
         expected = np.array([-np.cos(x) * np.cos(y) / 2, np.sin(x) * np.sin(y) / 2])
         assert np.allclose(res, expected, atol=tol, rtol=0)
 
+    @pytest.mark.slow
     def test_tf(self, approx_order, strategy, tol):
         """Tests that the output of the finite-difference transform
         can be differentiated using TF, yielding second derivatives."""

tests/gradients/test_parameter_shift.py

@@ -818,6 +818,7 @@ def cost_fn(x):
         )
         assert np.allclose(res, expected, atol=tol, rtol=0)
 
+    @pytest.mark.slow
     def test_tf(self, tol):
         """Tests that the output of the finite-difference transform
         can be differentiated using TF, yielding second derivatives."""
@@ -1130,6 +1131,7 @@ def test_autograd(self, tol):
         assert np.allclose(res[1][:, 2:5], np.zeros([2, 3, 3]), atol=tol, rtol=0)
         assert np.allclose(res[2][:, -1], np.zeros([2, 1, 1]), atol=tol, rtol=0)
 
+    @pytest.mark.slow
     def test_tf(self, tol):
         """Test gradient of multiple trainable Hamiltonian coefficients
         using tf"""
@@ -1176,6 +1178,7 @@ def test_torch(self, tol):
         assert np.allclose(hess[1][:, 2:5], np.zeros([2, 3, 3]), atol=tol, rtol=0)
         assert np.allclose(hess[2][:, -1], np.zeros([2, 1, 1]), atol=tol, rtol=0)
 
+    @pytest.mark.slow
     def test_jax(self, tol):
         """Test gradient of multiple trainable Hamiltonian coefficients
         using JAX"""

tests/gradients/test_vjp.py

@@ -259,6 +259,7 @@ def test_torch(self, tol):
         exp = qml.jacobian(lambda x: expected(x)[0])(params.detach().numpy())
         assert np.allclose(params.grad, exp, atol=tol, rtol=0)
 
+    @pytest.mark.slow
     def test_tf(self, tol):
         """Tests that the output of the VJP transform
         can be differentiated using TF."""
@@ -282,6 +283,7 @@ def test_tf(self, tol):
         res = t.jacobian(vjp, params)
         assert np.allclose(res, qml.jacobian(expected)(params.numpy()), atol=tol, rtol=0)
 
+    @pytest.mark.slow
     def test_jax(self, tol):
         """Tests that the output of the VJP transform
         can be differentiated using JAX."""

tests/interfaces/test_batch_tensorflow.py

@@ -668,6 +668,7 @@ def test_sampling(self, execute_kwargs):
 class TestHigherOrderDerivatives:
     """Test that the TensorFlow execute function can be differentiated"""
 
+    @pytest.mark.slow
     @pytest.mark.parametrize(
         "params",
         [

tests/interfaces/test_tape_tf.py

@@ -169,6 +169,7 @@ def test_jacobian_options(self, mocker, tol):
             assert args[1]["order"] == 2
             assert args[1]["h"] == 1e-8
 
+    @pytest.mark.slow
     def test_reusing_quantum_tape(self, tol):
         """Test re-using a quantum tape by passing new parameters"""
         a = tf.Variable(0.1, dtype=tf.float64)
@@ -518,6 +519,7 @@ def test_jacobian(self, mocker, tol):
 
         spy.assert_not_called()
 
+    @pytest.mark.slow
     def test_reusing_quantum_tape(self, mocker, tol):
         """Test re-using a quantum tape by passing new parameters"""
         spy = mocker.spy(JacobianTape, "jacobian")

tests/math/test_functions.py

@@ -675,6 +675,7 @@ def test_numpy(self, t):
         """Vanilla NumPy arrays, sequences, and lists will always return False"""
         assert not fn.requires_grad(t)
 
+    @pytest.mark.slow
     def test_jax(self):
         """JAX DeviceArrays differentiability depends on the argnums argument"""
         res = None
@@ -1368,6 +1369,7 @@ def circuit(weights):
         expected = self.expected_grad(weights)
         assert np.allclose(grad, expected, atol=tol, rtol=0)
 
+    @pytest.mark.slow
     def test_jax(self, tol):
         """Test that the covariance matrix computes the correct
         result, and is differentiable, using the JAX interface"""

tests/optimize/test_shot_adaptive.py

@@ -526,6 +526,7 @@ def circuit(x):
         assert np.allclose(circuit(params), -1, atol=0.1, rtol=0.2)
         assert opt.shots_used > min_shots
 
+    @pytest.mark.slow
     def test_vqe_optimization(self):
         """Test that a simple VQE circuit can be optimized"""
         dev = qml.device("default.qubit", wires=2, shots=100)

tests/pytest.ini

@@ -0,0 +1,3 @@
+[pytest]
+markers =
+    slow: marks tests as slow (deselect with '-m "not slow"')

tests/qnn/test_keras.py

@@ -404,6 +404,7 @@ def c_default(w1, w2, w3, w4, w5, w6, w7, inputs=None):
         assert layer_out.shape == (batch_size, output_dim)
         assert np.allclose(layer_out[0], c(x[0], *weights))
 
+    @pytest.mark.slow
     @pytest.mark.parametrize("n_qubits, output_dim", indices_up_to(2))
     @pytest.mark.parametrize("batch_size", [2, 4, 6])
     @pytest.mark.parametrize("middle_dim", [2, 5, 8])

tests/templates/test_subroutines/test_double_excitation.py

@@ -294,6 +294,7 @@ def test_autograd(self):
         # without error
         grad_fn(weight)
 
+    @pytest.mark.slow
     def test_jax(self):
         """Tests the jax interface."""
 

tests/templates/test_subroutines/test_ucssd.py

@@ -340,6 +340,7 @@ def test_autograd(self, tol):
 
         assert np.allclose(grads, grads2, atol=tol, rtol=0)
 
+    @pytest.mark.slow
     def test_jax(self, tol):
         """Tests the jax interface."""
 

tests/test_about.py

@@ -27,6 +27,7 @@
 import pennylane as qml
 
 
+@pytest.mark.slow
 def test_about():
     """
     about: Tests if the about string prints correct.

tests/test_vqe.py

@@ -366,6 +366,7 @@ def test_passing_kwargs(self, coeffs, observables, expected):
             assert qnode.diff_options["h"] == 123
             assert qnode.diff_options["order"] == 2
 
+    @pytest.mark.slow
     @pytest.mark.parametrize("interface", ["tf", "torch", "autograd"])
     def test_optimize(self, interface, tf_support, torch_support):
         """Test that an ExpvalCost with observable optimization gives the same result as another
@@ -519,6 +520,7 @@ def test_grad_zero_hamiltonian(self, opt):
         dc = qml.grad(cost)(w)
         assert np.allclose(dc, 0)
 
+    @pytest.mark.slow
     def test_optimize_grad_torch(self, torch_support):
         """Test that the gradient of ExpvalCost is accessible and correct when using observable
         optimization and the Torch interface."""
@@ -544,6 +546,7 @@ def test_optimize_grad_torch(self, torch_support):
 
         assert np.allclose(dc, big_hamiltonian_grad)
 
+    @pytest.mark.slow
     def test_optimize_grad_tf(self, tf_support):
         """Test that the gradient of ExpvalCost is accessible and correct when using observable
         optimization and the TensorFlow interface."""
@@ -827,6 +830,7 @@ def circuit(w):
         dc = qml.grad(circuit)(w)
         assert np.allclose(dc, 0, atol=tol)
 
+    @pytest.mark.slow
     def test_grad_torch(self, torch_support, tol):
         """Tests VQE gradients in the torch interface."""
         if not torch_support:
@@ -870,6 +874,7 @@ def circuit(w):
 
         assert np.allclose(dc, big_hamiltonian_grad, atol=tol)
 
+    @pytest.mark.slow
     def test_grad_jax(self, tol):
         """Tests VQE gradients in the jax interface."""
         jax = pytest.importorskip("jax")

tests/transforms/test_qmc_transform.py

@@ -96,6 +96,7 @@ def test_apply_controlled_v(n_wires):
 class TestApplyControlledQ:
     """Tests for the apply_controlled_Q function"""
 
+    @pytest.mark.slow
     @pytest.mark.parametrize("n_wires", range(2, 5))
     def test_apply(self, n_wires):
         """Test if the apply_controlled_Q performs the correct transformation by reconstructing the
@@ -137,6 +138,7 @@ def test_raises(self):
 class TestQuantumMonteCarlo:
     """Tests for the quantum_monte_carlo function"""
 
+    @pytest.mark.slow
     @pytest.mark.parametrize("n_wires", range(2, 4))
     def test_apply(self, n_wires):
         """Test if the quantum_monte_carlo performs the correct transformation by reconstructing the
@@ -181,6 +183,7 @@ def test_shared_wires(self):
                 lambda: None, wires=wires, target_wire=0, estimation_wires=estimation_wires
             )
 
+    @pytest.mark.slow
     def test_integration(self):
         """Test if quantum_monte_carlo generates the correct circuit by comparing it to the
         QuantumMonteCarlo template on the practical example specified in the usage details. Custom