Merge Canonicalize slice and useless slice rewrites

Author: Dhruvanshu-Joshi

pytensor/tensor/rewriting/subtensor.py

@@ -336,98 +336,68 @@ def local_subtensor_of_dot(fgraph, node):
 @register_useless
 @register_canonicalize
 @register_specialize
+@register_stabilize
 @node_rewriter([Subtensor])
 def local_useless_slice(fgraph, node):
     """
     Remove Subtensor of the form:
         1. X[0, :] -> X[0]
         2. X[:] -> X
 
-    """
-    idxs = get_idx_list(node.inputs, node.op.idx_list)
-
-    if not idxs:
-        return [node.inputs[0]]
-
-    last_useless_slice = len(idxs)
-    for s in idxs[::-1]:
-        # check if slice and then check slice indices
-        if (
-            isinstance(s, slice)
-            and s.start is None
-            and s.stop is None
-            and (
-                s.step is None
-                or extract_constant(s.step, only_process_constants=True) == 1
-            )
-        ):
-            last_useless_slice -= 1
-        else:
-            break
-    # check if we removed something
-    if last_useless_slice < len(idxs):
-        new_idxs = idxs[:last_useless_slice]
-
-        if new_idxs:
-            new_subtensor = Subtensor(new_idxs)
-            new_subtensor_inputs = get_slice_elements(
-                new_idxs, lambda x: isinstance(x, Variable)
-            )
-            out = new_subtensor(node.inputs[0], *new_subtensor_inputs)
-            # Copy over previous output stacktrace
-            copy_stack_trace(node.outputs, out)
-            return [out]
-        else:
-            # Subtensor is not needed at all
-            return [node.inputs[0]]
-
-
-@register_useless
-@register_canonicalize
-@register_stabilize
-@register_specialize
-@node_rewriter([Subtensor])
-def local_replace_slice(fgraph, node):
-    """
-    Rewrite Subtensor of the form:
+    Also, rewrite Subtensor of the form:
         X[0:7:1] -> X[None:None:None]
-    where X is a vector of length 7
+        where X is a vector of length 7
 
     """
     idxs = get_idx_list(node.inputs, node.op.idx_list)
     x = node.inputs[0]
 
     if not idxs:
-        return
+        return [node.inputs[0]]
 
     new_idxs = list(idxs)
-    idx_flag = False
+    change_flag = False
+    last_useful_idx = -1
     for dim, s in enumerate(new_idxs):
         if not isinstance(s, slice):
+            last_useful_idx = dim
+            continue
+
+        if s == slice(None):
             continue
 
         start = s.start
         stop = s.stop
         step = s.step
-        if extract_constant(start, only_process_constants=True) == 0:
-            idx_flag = True
+        if (
+            start is not None
+            and extract_constant(start, only_process_constants=True) == 0
+        ):
+            change_flag = True
             start = None
 
         if (
-            x.type.shape[dim] is not None
+            stop is not None
+            and x.type.shape[dim] is not None
             and extract_constant(stop, only_process_constants=True) == x.type.shape[dim]
         ):
-            idx_flag = True
+            change_flag = True
             stop = None
 
-        if extract_constant(step, only_process_constants=True) == 1:
-            idx_flag = True
+        if (
+            step is not None
+            and extract_constant(step, only_process_constants=True) == 1
+        ):
+            change_flag = True
             step = None
 
+        if not (start is None and stop is None and step is None):
+            last_useful_idx = dim
+
         new_idxs[dim] = slice(start, stop, step)
 
-    if idx_flag is True:
-        out = x[tuple(new_idxs)]
+    if change_flag or ((last_useful_idx + 1) < len(idxs)):
+        out = x[tuple(new_idxs[: last_useful_idx + 1])]
         # Copy over previous output stacktrace
         copy_stack_trace(node.outputs, out)
 

tests/tensor/rewriting/test_subtensor.py

@@ -2404,22 +2404,36 @@ def test_local_blockwise_advanced_inc_subtensor(set_instead_of_inc):
     np.testing.assert_allclose(fn(test_x, test_y), expected_out)
 
 
-@pytest.mark.parametrize("fstop, lstop, lstep", [(None, 9, 1), (-1, -1, -1)])
-def test_slice_canonicalize(fstop, lstop, lstep):
+def test_slice_canonicalize():
+    rng = np.random.default_rng(43)
     x = tensor(shape=(3, 5, None, 9))
-    y = x[0:fstop, 0:5, 0:7, 0:lstop:lstep]
-    f = pytensor.function([x], y)
-    test_y = f.maker.fgraph.toposort()
+    test_x = rng.normal(size=(3, 5, 8, 9))
+    # Test case 1
+    y = x[0:None, 0:5, 0:7, 0:9:1]
+    f = pytensor.function([x], y, allow_input_downcast=True)
+    test_y = f.maker.fgraph.outputs[0].owner.inputs[0]
 
-    y1 = x[None:None:None, None:None:None, None:7:None, None:None:None]
+    expected_y = x[None:None:None, None:None:None, None:7:None]
 
-    if fstop == -1 and lstop == -1 and lstep == -1:
-        y1 = x[None:-1:None, None:None:None, None:7:None, None:-1:-1]
+    assert equal_computations([test_y], [expected_y])
 
-    f1 = pytensor.function([x], y1)
-    expected_y = f1.maker.fgraph.toposort()
+    np.testing.assert_allclose(
+        f(test_x),
+        test_x[
+            0:None, 0:5, 0:7, 0:9:1
+        ],  # Use the unoptimized slice to make sure our rewrite logic is correct
+    )
+
+    # Test case 2
+    y1 = x[0:-1, 0:5, 0:7, 0:-1:-1]
+    f1 = pytensor.function([x], y1, allow_input_downcast=True)
+    test_y1 = f1.maker.fgraph.outputs[0].owner.inputs[0]
+
+    expected_y1 = x[None:-1:None, None:None:None, None:7:None, None:-1:-1]
+
+    assert equal_computations([test_y1], [expected_y1])
 
-    assert all(
-        equal_computations([x1], [y1])
-        for x1, y1 in zip(test_y[0].inputs, expected_y[0].inputs)
+    np.testing.assert_allclose(
+        f1(test_x),
+        test_x[0:-1, 0:5, 0:7, 0:-1:-1],
     )