Move `test_local_subtensor_of_dot` to `test_subtensor_lift`

tests/tensor/rewriting/test_math.py

@@ -1203,52 +1203,6 @@ def test_local_log_add_exp():
     # TODO: test that the rewrite works in the presence of broadcasting.
 
 
-def test_local_subtensor_of_dot():
-    m1 = matrix()
-    m2 = matrix()
-    d1 = np.arange(6).reshape((3, 2)).astype(config.floatX)
-    d2 = np.arange(8).reshape((2, 4)).astype(config.floatX) + 10
-    mode = get_default_mode().including("local_subtensor_of_dot")
-
-    def test_equality(a, b):
-        return a.shape == b.shape and np.allclose(a, b)
-
-    # [cst]
-    f = function([m1, m2], pytensor.tensor.dot(m1, m2)[1], mode=mode)
-    topo = f.maker.fgraph.toposort()
-    assert test_equality(f(d1, d2), np.dot(d1, d2)[1])
-    # DimShuffle happen in FAST_COMPILE
-    assert isinstance(topo[-1].op, CGemv | Gemv | DimShuffle)
-
-    # slice
-    f = function([m1, m2], pytensor.tensor.dot(m1, m2)[1:2], mode=mode)
-    topo = f.maker.fgraph.toposort()
-    assert test_equality(f(d1, d2), np.dot(d1, d2)[1:2])
-    assert isinstance(topo[-1].op, Dot22)
-
-    m1 = tensor3()
-    m2 = tensor3()
-    idx = iscalar()
-    d1 = np.arange(30).reshape(2, 5, 3).astype(config.floatX)
-    d2 = np.arange(72).reshape(4, 3, 6).astype(config.floatX) + 100
-
-    f = function(
-        [m1, m2, idx], pytensor.tensor.dot(m1, m2)[idx, 1:4, :, idx:], mode=mode
-    )
-    assert test_equality(f(d1, d2, 1), np.dot(d1, d2)[1, 1:4, :, 1:])
-    # if we return the gradients. We need to use same mode as before.
-    assert check_stack_trace(f, ops_to_check="last")
-
-    f = function(
-        [m1, m2, idx], pytensor.tensor.dot(m1, m2)[1:4, :, idx:, idx], mode=mode
-    )
-    assert test_equality(f(d1, d2, 1), np.dot(d1, d2)[1:4, :, 1:, 1])
-
-    # Now test that the stack trace is copied over properly,
-    # if we return the gradients. We need to use same mode as before.
-    assert check_stack_trace(f, ops_to_check="last")
-
-
 def test_local_elemwise_sub_zeros():
     scal = scalar()
     vect = vector()

tests/tensor/rewriting/test_subtensor_lift.py

@@ -37,6 +37,8 @@ from pytensor.tensor import (
     vector,
 )
 from pytensor.tensor.basic import MakeVector, concatenate, expand_dims, make_vector
+from pytensor.tensor.blas import Dot22, Gemv
+from pytensor.tensor.blas_c import CGemv
 from pytensor.tensor.elemwise import DimShuffle, Elemwise
 from pytensor.tensor.math import sum as pt_sum
 from pytensor.tensor.rewriting.subtensor_lift import (
@@ -178,6 +180,48 @@ class TestLocalSubtensorOfElemwise:
         assert local_subtensor_of_elemwise.transform(fgraph, out2.owner) is not None
 
 
+def test_local_subtensor_of_dot():
+    m1 = matrix()
+    m2 = matrix()
+    d1 = np.arange(6).reshape((3, 2)).astype(config.floatX)
+    d2 = np.arange(8).reshape((2, 4)).astype(config.floatX) + 10
+    mode = get_default_mode().including("local_subtensor_of_dot")
+
+    def test_equality(a, b):
+        return a.shape == b.shape and np.allclose(a, b)
+
+    # [cst]
+    f = function([m1, m2], pt.dot(m1, m2)[1], mode=mode)
+    topo = f.maker.fgraph.toposort()
+    assert test_equality(f(d1, d2), np.dot(d1, d2)[1])
+    # DimShuffle happen in FAST_COMPILE
+    assert isinstance(topo[-1].op, CGemv | Gemv | DimShuffle)
+
+    # slice
+    f = function([m1, m2], pt.dot(m1, m2)[1:2], mode=mode)
+    topo = f.maker.fgraph.toposort()
+    assert test_equality(f(d1, d2), np.dot(d1, d2)[1:2])
+    assert isinstance(topo[-1].op, Dot22)
+
+    m1 = tensor3()
+    m2 = tensor3()
+    idx = iscalar()
+    d1 = np.arange(30).reshape(2, 5, 3).astype(config.floatX)
+    d2 = np.arange(72).reshape(4, 3, 6).astype(config.floatX) + 100
+
+    f = function([m1, m2, idx], pt.dot(m1, m2)[idx, 1:4, :, idx:], mode=mode)
+    assert test_equality(f(d1, d2, 1), np.dot(d1, d2)[1, 1:4, :, 1:])
+    # if we return the gradients. We need to use same mode as before.
+    assert check_stack_trace(f, ops_to_check="last")
+
+    f = function([m1, m2, idx], pt.dot(m1, m2)[1:4, :, idx:, idx], mode=mode)
+    assert test_equality(f(d1, d2, 1), np.dot(d1, d2)[1:4, :, 1:, 1])
+
+    # Now test that the stack trace is copied over properly,
+    # if we return the gradients. We need to use same mode as before.
+    assert check_stack_trace(f, ops_to_check="last")
+
+
 @pytest.mark.parametrize(
     "original_fn, expected_fn",
     [