Make use of the relevant optimization explicit in TestLocalSubtensorMakeVector

tests/tensor/test_subtensor_opt.py

@@ -560,21 +560,24 @@ class TestSubtensorIncSubtensor:
 
 
 class TestLocalSubtensorMakeVector:
+
+    mode = get_mode("FAST_RUN").including("local_subtensor_make_vector")
+
     def test_scalar_idx(self):
         x, y, z = lscalars("xyz")
         v = make_vector(x, y, z)
-        f = function([x, y, z], v[0], mode=mode_opt)
+        f = function([x, y, z], v[0], mode=self.mode)
 
         prog = f.maker.fgraph.toposort()
         assert len(prog) == 1
         assert isinstance(prog[0].op, DeepCopyOp)
         assert f(0, 1, 2) == 0
 
-    def test_idx_sybmolic(self):
+    def test_idx_symbolic(self):
         x, y, z = iscalars("xyz")
         v = MakeVector("int32")(x, y, z)
         idx = aet.as_tensor([0], dtype=np.int64)
-        f = function([x, y, z], v[idx], mode=mode_opt)
+        f = function([x, y, z], v[idx], mode=self.mode)
 
         opt_fgraph = f.maker.fgraph
         assert opt_fgraph.outputs[0].dtype == "int32"
@@ -585,7 +588,7 @@ class TestLocalSubtensorMakeVector:
     def test_slice_idx_start(self):
         x, y, z = iscalars("xyz")
         v = MakeVector("int32")(x, y, z)
-        f = function([x, y, z], v[1:], mode=mode_opt, on_unused_input="ignore")
+        f = function([x, y, z], v[1:], mode=self.mode, on_unused_input="ignore")
 
         opt_fgraph = f.maker.fgraph
         assert opt_fgraph.outputs[0].dtype == "int32"
@@ -597,7 +600,7 @@ class TestLocalSubtensorMakeVector:
     def test_slice_idx_stop(self):
         x, y, z = lscalars("xyz")
         v = make_vector(x, y, z)
-        f = function([x, y, z], v[:2], mode=mode_opt)
+        f = function([x, y, z], v[:2], mode=self.mode)
 
         prog = f.maker.fgraph.toposort()
         assert len(prog) == 1
@@ -609,7 +612,7 @@ class TestLocalSubtensorMakeVector:
     def test_slice_idx_step(self):
         x, y, z = lscalars("xyz")
         v = make_vector(x, y, z)
-        f = function([x, y, z], v[::2], mode=mode_opt)
+        f = function([x, y, z], v[::2], mode=self.mode)
 
         prog = f.maker.fgraph.toposort()
         assert len(prog) == 1
@@ -621,7 +624,7 @@ class TestLocalSubtensorMakeVector:
     def test_AdvancedSubtensor1_idx(self):
         x, y, z = lscalars("xyz")
         v = make_vector(x, y, z)
-        f = function([x, y, z], v[[0, 2]], mode=mode_opt)
+        f = function([x, y, z], v[[0, 2]], mode=self.mode)
 
         prog = f.maker.fgraph.toposort()
         assert len(prog) == 1
@@ -634,7 +637,7 @@ class TestLocalSubtensorMakeVector:
         x, y, z, q = lscalars("xyzq")
         v = make_vector(x, y, z)
         q = make_vector(0, 2)
-        f = function([x, y, z], v[q], mode=mode_opt)
+        f = function([x, y, z], v[q], mode=self.mode)
 
         prog = f.maker.fgraph.toposort()
         assert len(prog) == 1