Solving some test issues

Author: Dhruvanshu-Joshi

tests/link/numba/test_elemwise.py

@@ -146,7 +146,7 @@ def test_elemwise_speed(benchmark):
 
 
 @pytest.mark.parametrize(
-    "v, new_order",
+    "test_values, new_order",
     [
         # `{'drop': [], 'shuffle': [], 'augment': [0, 1]}`
         (
@@ -204,14 +204,15 @@ def test_elemwise_speed(benchmark):
         ),
     ],
 )
-def test_Dimshuffle(v, new_order):
+def test_Dimshuffle(test_values, new_order):
+    v = next(iter(test_values.keys()))
     g = v.dimshuffle(new_order)
     g_fg = FunctionGraph(outputs=[g])
     compare_numba_and_py(
         g_fg,
         [
-            i.tag.test_value
-            for i in g_fg.inputs
+            test_values[i]
+            for i in test_values
             if not isinstance(i, SharedVariable | Constant)
         ],
     )

tests/scan/test_basic.py

@@ -2241,38 +2241,42 @@ def test_compute_test_value_grad():
     """
     See https://groups.google.com/d/msg/theano-users/fAP3i2CbskQ/3OgBf4yjqiQJ
     """
-    # WEIGHT = np.array([1, 2, 1, 3, 4, 1, 5, 6, 1, 7, 8, 1], dtype="float32")
+    WEIGHT = np.array([1, 2, 1, 3, 4, 1, 5, 6, 1, 7, 8, 1], dtype="float32")
 
-    with config.change_flags(exception_verbosity="high"):
-        W_flat = fvector(name="W")
-        W = W_flat.reshape((2, 2, 3))
+    with config.change_flags(compute_test_value="raise", exception_verbosity="high"):
+        with pytest.warns(FutureWarning):
+            W_flat = fvector(name="W")
+            W_flat.tag.test_value = WEIGHT
+            W = W_flat.reshape((2, 2, 3))
 
-        outputs_mi = pt.as_tensor_variable(np.asarray(0, dtype="float32"))
+            outputs_mi = pt.as_tensor_variable(np.asarray(0, dtype="float32"))
+            outputs_mi.tag.test_value = np.asarray(0, dtype="float32")
 
-        def loss_mi(mi, sum_mi, W):
-            outputs_ti = pt.as_tensor_variable(np.asarray(0, dtype="float32"))
+            def loss_mi(mi, sum_mi, W):
+                outputs_ti = pt.as_tensor_variable(np.asarray(0, dtype="float32"))
+                outputs_ti.tag.test_value = np.asarray(0, dtype="float32")
 
-            def loss_ti(ti, sum_ti, mi, W):
-                return W.sum().sum().sum() + sum_ti
+                def loss_ti(ti, sum_ti, mi, W):
+                    return W.sum().sum().sum() + sum_ti
 
-            result_ti, _ = scan(
-                fn=loss_ti,
-                outputs_info=outputs_ti,
-                sequences=pt.arange(W.shape[1], dtype="int32"),
-                non_sequences=[mi, W],
-            )
-            lossmi = result_ti[-1]
-            return sum_mi + lossmi
+                result_ti, _ = scan(
+                    fn=loss_ti,
+                    outputs_info=outputs_ti,
+                    sequences=pt.arange(W.shape[1], dtype="int32"),
+                    non_sequences=[mi, W],
+                )
+                lossmi = result_ti[-1]
+                return sum_mi + lossmi
 
-        result_mi, _ = scan(
-            fn=loss_mi,
-            outputs_info=outputs_mi,
-            sequences=pt.arange(W.shape[0], dtype="int32"),
-            non_sequences=[W],
-        )
+            result_mi, _ = scan(
+                fn=loss_mi,
+                outputs_info=outputs_mi,
+                sequences=pt.arange(W.shape[0], dtype="int32"),
+                non_sequences=[W],
+            )
 
-        loss = result_mi[-1]
-        grad(loss, W_flat)
+            loss = result_mi[-1]
+            grad(loss, W_flat)
 
 
 @pytest.mark.xfail(reason="NominalVariables don't support test values")
@@ -2281,23 +2285,28 @@ def test_compute_test_value_grad_cast():
 
     See https://groups.google.com/d/topic/theano-users/o4jK9xDe5WI/discussion
     """
-    h = matrix("h")
-    with pytest.warns(FutureWarning):
-        h.tag.test_value = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=config.floatX)
+    with config.change_flags(compute_test_value="raise"):
+        with pytest.warns(FutureWarning):
+            h = matrix("h")
+            h.tag.test_value = np.array(
+                [[1, 2, 3, 4], [5, 6, 7, 8]], dtype=config.floatX
+            )
 
-    w = shared(
-        np.random.default_rng(utt.fetch_seed()).random((4, 3)).astype(config.floatX),
-        name="w",
-    )
+            w = shared(
+                np.random.default_rng(utt.fetch_seed())
+                .random((4, 3))
+                .astype(config.floatX),
+                name="w",
+            )
 
-    outputs, _ = scan(
-        lambda i, h, w: (dot(h[i], w), i),
-        outputs_info=[None, 0],
-        non_sequences=[h, w],
-        n_steps=3,
-    )
+            outputs, _ = scan(
+                lambda i, h, w: (dot(h[i], w), i),
+                outputs_info=[None, 0],
+                non_sequences=[h, w],
+                n_steps=3,
+            )
 
-    grad(outputs[0].sum(), w)
+            grad(outputs[0].sum(), w)
 
 
 def test_constant_folding_n_steps():