Stop checking for input alias in Function.__call__

Author: ricardoV94

Diff for: pytensor/compile/function/types.py

@@ -393,41 +393,6 @@ def __init__(
         assert len(self.input_storage) == len(self.maker.fgraph.inputs)
         assert len(self.output_storage) == len(self.maker.fgraph.outputs)
 
-        # Group indexes of inputs that are potentially aliased to each other
-        # Note: Historically, we only worried about aliasing inputs if they belonged to the same type,
-        #  even though there could be two distinct types that use the same kinds of underlying objects.
-        potential_aliased_input_groups = []
-        for inp in maker.inputs:
-            # If the input is a shared variable, the memory region is under PyTensor control
-            # and can't be aliased.
-            if not (
-                isinstance(inp, In)
-                and inp.borrow
-                and not inp.shared
-                and hasattr(inp.variable.type, "may_share_memory")
-            ):
-                continue
-
-            for group in potential_aliased_input_groups:
-                # If one is super of the other, that means one could be replaced by the other
-                if any(
-                    inp.variable.type.is_super(other_inp.variable.type)
-                    or other_inp.variable.type.is_super(inp.variable.type)
-                    for other_inp in group
-                ):
-                    group.append(inp)
-                    break
-            else:  # no break
-                # Input makes a new group
-                potential_aliased_input_groups.append([inp])
-
-        # Potential aliased inputs are those that belong to the same group
-        self._potential_aliased_input_groups: tuple[tuple[int, ...], ...] = tuple(
-            tuple(maker.inputs.index(inp) for inp in group)
-            for group in potential_aliased_input_groups
-            if len(group) > 1
-        )
-
         # We will be popping stuff off this `containers` object.  It is a copy.
         containers = list(self.input_storage)
         finder = {}
@@ -844,11 +809,18 @@ def __call__(self, *args, **kwargs):
             if self.output_keys is not None:
                 output_subset = [self.output_keys.index(key) for key in output_subset]
 
-        # Reinitialize each container's 'provided' counter
         if self.trust_input:
+            # Set positional arguments
             for arg_container, arg in zip(input_storage, args, strict=False):
                 arg_container.storage[0] = arg
+
+            # Set keyword arguments
+            if kwargs:  # for speed, skip the items for empty kwargs
+                for k, arg in kwargs.items():
+                    self[k] = arg
+
         else:
+            # Reinitialize each container's 'provided' counter
             for arg_container in input_storage:
                 arg_container.provided = 0
 
@@ -899,39 +871,10 @@ def __call__(self, *args, **kwargs):
                         raise
                 arg_container.provided += 1
 
-        # Set keyword arguments
-        if kwargs:  # for speed, skip the items for empty kwargs
-            for k, arg in kwargs.items():
-                self[k] = arg
-
-        if not self.trust_input:
-            # Collect aliased inputs among the storage space
-            for potential_group in self._potential_aliased_input_groups:
-                args_share_memory: list[list[int]] = []
-                for i in potential_group:
-                    i_type = self.maker.inputs[i].variable.type
-                    i_val = input_storage[i].storage[0]
-
-                    # Check if value is aliased with any of the values in one of the groups
-                    for j_group in args_share_memory:
-                        if any(
-                            i_type.may_share_memory(input_storage[j].storage[0], i_val)
-                            for j in j_group
-                        ):
-                            j_group.append(i)
-                            break
-                    else:  # no break
-                        # Create a new group
-                        args_share_memory.append([i])
-
-                # Check for groups of more than one argument that share memory
-                for group in args_share_memory:
-                    if len(group) > 1:
-                        # copy all but the first
-                        for i in group[1:]:
-                            input_storage[i].storage[0] = copy.copy(
-                                input_storage[i].storage[0]
-                            )
+            # Set keyword arguments
+            if kwargs:  # for speed, skip the items for empty kwargs
+                for k, arg in kwargs.items():
+                    self[k] = arg
 
             # Check if inputs are missing, or if inputs were set more than once, or
             # if we tried to provide inputs that are supposed to be implicit.

Diff for: tests/compile/function/test_pfunc.py

@@ -732,16 +732,13 @@ class TestAliasingRules:
     # 2. shared variables are allocated in this memory space, as are the
     # temporaries used for Function evaluation.
     #
-    # 3. Physically, this managed memory space may be spread across the host,
-    # on a GPU device(s), or even on a remote machine.
-    #
-    # 4. PyTensor assumes that shared variables are never aliased to one another,
+    # 3. PyTensor assumes that shared variables are never aliased to one another,
     # and tries to make it impossible to accidentally alias them.
     #
-    # 5. PyTensor's managed data is constant while PyTensor Functions are not running
+    # 4. PyTensor's managed data is constant while PyTensor Functions are not running
     # and pytensor library code is not running.
     #
-    # 6. The default behaviour of Function is to return user-space values for
+    # 5. The default behaviour of Function is to return user-space values for
     # outputs, but this can be overridden (borrow=True) for better performance,
     # in which case the returned value may be aliased to managed memory, and
     # potentially invalidated by the next PyTensor Function call or call to pytensor
@@ -810,6 +807,9 @@ def test_sparse_input_aliasing_affecting_inplace_operations(self):
         assert np.allclose(vals.todense(), bogus_vals.todense())
 
     def test_input_aliasing_affecting_inplace_operations(self):
+        # Note: The input aliasing check was disabled, so this test now just confirms that wrong values
+        # will be obtained if the inputs are aliased.
+
         # Note: to trigger this bug with pytensor rev 4586:2bc6fc7f218b,
         #        you need to make in inputs mutable (so that inplace
         #        operations are used) and to break the elemwise composition
@@ -860,9 +860,12 @@ def test_input_aliasing_affecting_inplace_operations(self):
         v_copy = v.copy()
         vals = f(v, v_copy, m, m_copy)
 
-        assert np.allclose(vals, bogus_vals)
+        assert not np.allclose(vals, bogus_vals)
 
     def test_partial_input_aliasing_affecting_inplace_operations(self):
+        # Note: The input aliasing check was disabled, so this test now just confirms that wrong values
+        # will be obtained if the inputs are aliased.
+
         # Note: to trigger this bug with pytensor rev 4586:2bc6fc7f218b,
         #        you need to make in inputs mutable ( so that inplace
         #        operations are used) and to break the elemwise composition
@@ -906,7 +909,7 @@ def test_partial_input_aliasing_affecting_inplace_operations(self):
         v_copy2 = v.copy()
         vals = f(v[:2], v_copy1[1:3], v_copy2[2:4], m, m_copy1, m_copy2)
 
-        assert np.allclose(vals, bogus_vals)
+        assert not np.allclose(vals, bogus_vals)
 
     def test_potential_output_aliasing_induced_by_updates(self):
         A = self.shared(np.zeros((2, 2)))

Diff for: tests/compile/function/test_types.py

@@ -752,52 +752,6 @@ def test_default_values(self):
         except TypeError:
             assert funct(first=1) == x
 
-    def test_check_for_aliased_inputs(self):
-        b = np.random.random((5, 4))
-        s1 = shared(b)
-        s2 = shared(b)
-        x1 = vector()
-        x2 = vector(shape=(3,))
-        x3 = vector(shape=(1,))
-
-        # Assert cases we should not check for aliased inputs
-        for d in [
-            dict(outputs=[s1 + 1]),
-            dict(outputs=[s1 + 1, s2 + 3]),
-            dict(outputs=[s1 + 1], updates=[(s2, s2 + 3)]),
-            dict(inputs=[x1], outputs=[x1 + 1], updates=[(s2, s2 + 3)]),
-            dict(
-                inputs=[In(x1, mutable=True)], outputs=[x1 + 1], updates=[(s2, s2 + 3)]
-            ),
-            dict(
-                inputs=[In(x2, mutable=True), In(x3, mutable=True)],
-                outputs=[x2 + 2, x3 + 3],
-            ),
-        ]:
-            if "inputs" not in d:
-                d["inputs"] = []
-            f = function(**d)
-            assert not f._potential_aliased_input_groups, d
-
-        # Assert cases we should check for aliased inputs
-        for d in [
-            dict(
-                inputs=[In(x1, mutable=True), In(x2, mutable=True)],
-                outputs=[x1 + 1, x2 + 2],
-                updates=[(s2, s2 + 3)],
-            ),
-            dict(
-                inputs=[In(x1, mutable=True), In(x3, mutable=True)],
-                outputs=[x1 + 1, x3 + 3],
-                updates=[(s2, s2 + 3)],
-            ),
-        ]:
-            if "inputs" not in d:
-                d["inputs"] = []
-            f = function(**d)
-
-            assert f._potential_aliased_input_groups, d
-
     def test_output_dictionary(self):
         # Tests that function works when outputs is a dictionary
 
@@ -939,12 +893,6 @@ def test_deepcopy(self):
         assert x not in g.container
         assert x not in g.value
         assert len(f.defaults) == len(g.defaults)
-        # Shared variable is the first input
-        assert (
-            f._potential_aliased_input_groups
-            == g._potential_aliased_input_groups
-            == ((1, 2),)
-        )
         assert f.name == g.name
         assert f.maker.fgraph.name == g.maker.fgraph.name
         # print(f"{f.defaults = }")