Inplace Blockwise and core versions of Cholesky and Solve Ops.

Co-authored-by: Ricardo Vieira <28983449+ricardov94@users.noreply.github.com>

pytensor/graph/op.py

@@ -583,6 +583,12 @@ def make_thunk(
         )
         return self.make_py_thunk(node, storage_map, compute_map, no_recycling)
 
+    def inplace_on_inputs(self, allowed_inplace_inputs: list[int]) -> "Op":
+        """Try to return a version of self that tries to inplace in as many as `allowed_inplace_inputs`."""
+        # TODO: Document this in the Create your own Op docs
+        # By default, do nothing
+        return self
+
     def __str__(self):
         return getattr(type(self), "__name__", super().__str__())
 

pytensor/tensor/blockwise.py

@@ -45,6 +45,7 @@ def __init__(
         signature: str | None = None,
         name: str | None = None,
         gufunc_spec: tuple[str, int, int] | None = None,
+        destroy_map=None,
         **kwargs,
     ):
         """
@@ -79,6 +80,15 @@ def __init__(
         self.inputs_sig, self.outputs_sig = _parse_gufunc_signature(signature)
         self.gufunc_spec = gufunc_spec
         self._gufunc = None
+        if destroy_map is not None:
+            self.destroy_map = destroy_map
+        if self.destroy_map != core_op.destroy_map:
+            # Note: Should be fine for destroy_map of Blockwise to be more extensive than that of core_op
+            # But we are not using that anywhere yet, so this check is fine for now
+            raise ValueError(
+                f"Blockwise destroy_map {self.destroy_map} must be the same as that of the core_op {core_op} {core_op.destroy_map}"
+            )
+
         super().__init__(**kwargs)
 
     def __getstate__(self):

pytensor/tensor/rewriting/blockwise.py

@@ -1,7 +1,10 @@
+import itertools
+
+from pytensor.compile import Supervisor
 from pytensor.compile.mode import optdb
 from pytensor.graph import Constant, node_rewriter
 from pytensor.graph.replace import vectorize_node
-from pytensor.graph.rewriting.basic import copy_stack_trace, out2in
+from pytensor.graph.rewriting.basic import copy_stack_trace, in2out, out2in
 from pytensor.tensor.basic import Alloc, ARange, alloc, shape_padleft
 from pytensor.tensor.blockwise import Blockwise
 from pytensor.tensor.math import Dot
@@ -50,13 +53,14 @@ def local_useless_unbatched_blockwise(fgraph, node):
 
 
 # We register this rewrite late, so that other rewrites need only target Blockwise Ops
+# We do it after position>=60 so that Blockwise inplace rewrites will work also on useless Blockwise Ops
 optdb.register(
     "local_useless_unbatched_blockwise",
     out2in(local_useless_unbatched_blockwise, ignore_newtrees=True),
     "fast_run",
     "fast_compile",
     "blockwise",
-    position=49,
+    position=60,
 )
 
 
@@ -225,3 +229,77 @@ def local_blockwise_reshape(fgraph, node):
         new_out = x.reshape([*tuple(batched_shape), *tuple(core_reshape)])
         copy_stack_trace(node.outputs[0], new_out)
         return [new_out]
+
+
+@node_rewriter(tracks=[Blockwise], inplace=True)
+def blockwise_inplace(fgraph, node):
+    blockwise_op = node.op
+
+    if blockwise_op.destroy_map:
+        # Op already has inplace
+        return
+
+    # Find out valid inputs for inplacing
+    batch_ndim = blockwise_op.batch_ndim(node)
+    out_batch_bcast = node.outputs[0].type.broadcastable[:batch_ndim]
+
+    protected_inputs = [
+        f.protected for f in fgraph._features if isinstance(f, Supervisor)
+    ]
+    protected_inputs = list(itertools.chain.from_iterable(protected_inputs))
+    protected_inputs.extend(fgraph.outputs)
+    allowed_inplace_inputs = [
+        idx
+        for idx, inp in enumerate(node.inputs)
+        if
+        (
+            # Constants would need to be recreated every time if inplaced
+            not isinstance(inp, Constant)
+            # We can only inplace on inputs that are not being broadcasted
+            # As those are reused across iterations of Blockwise
+            and node.inputs[idx].type.broadcastable[:batch_ndim] == out_batch_bcast
+            # Inputs that are marked as protected or destroyed can't be inplaced
+            and not fgraph.has_destroyers([inp])
+            and inp not in protected_inputs
+        )
+    ]
+
+    if not allowed_inplace_inputs:
+        return None
+
+    inplace_core_op = blockwise_op.core_op.inplace_on_inputs(
+        allowed_inplace_inputs=allowed_inplace_inputs
+    )
+
+    if not inplace_core_op.destroy_map:
+        return None
+
+    # Check Op is not trying to inplace on non-candidate inputs
+    for destroyed_inputs in inplace_core_op.destroy_map.values():
+        for destroyed_input in destroyed_inputs:
+            if destroyed_input not in allowed_inplace_inputs:
+                raise ValueError(
+                    f"Op {blockwise_op.core_op} destroy_map does not respect allowed_inplace_inputs {allowed_inplace_inputs}"
+                )
+
+    # Recreate core_op with inplace
+    inplace_blockwise_op = Blockwise(
+        core_op=inplace_core_op,
+        signature=blockwise_op.signature,
+        name=blockwise_op.name,
+        gufunc_spec=blockwise_op.gufunc_spec,
+        destroy_map=inplace_core_op.destroy_map,
+    )
+
+    out = inplace_blockwise_op.make_node(*node.inputs).outputs
+    copy_stack_trace(node.outputs, out)
+    return out
+
+
+optdb.register(
+    "blockwise_inplace",
+    in2out(blockwise_inplace),
+    "fast_run",
+    "inplace",
+    position=50.1,
+)