Generalize dot rewrites to work with Blockwise

Author: ricardoV94

pytensor/tensor/rewriting/math.py

@@ -44,6 +44,7 @@
     Prod,
     Sum,
     _conj,
+    _dot,
     _inner_prod,
     _matrix_matrix_matmul,
     _matrix_vec_prod,
@@ -97,6 +98,7 @@
     register_useless,
 )
 from pytensor.tensor.rewriting.elemwise import apply_local_dimshuffle_lift
+from pytensor.tensor.rewriting.linalg import is_matrix_transpose
 from pytensor.tensor.shape import Shape, Shape_i
 from pytensor.tensor.subtensor import Subtensor
 from pytensor.tensor.type import (
@@ -174,21 +176,20 @@ def local_lift_transpose_through_dot(fgraph, node):
     These rewrites "lift" (propagate towards the inputs) `DimShuffle`
     through dot product.  It allows to put the graph in a more standard shape,
     and to later merge consecutive `DimShuffle`\s.
-
-    The transformation should be apply whether or not the transpose is
-    inplace.  The newly-introduced transpositions are not inplace, this will
-    be taken care of in a later rewrite phase.
-
     """
-    if not (isinstance(node.op, DimShuffle) and node.op.new_order == (1, 0)):
-        return False
-    if not (node.inputs[0].owner and isinstance(node.inputs[0].owner.op, Dot)):
+
+    if not (
+        is_matrix_transpose(node.out)
+        and node.inputs[0].owner
+        and ((dot_op := node.inputs[0].owner.op) in (_dot, _matmul))
+    ):
         return False
+
     x, y = node.inputs[0].owner.inputs
 
-    if x.ndim == y.ndim == 2:
+    if x.ndim >= y.ndim >= 2:
         # Output is dot product of transposed inputs in reverse order
-        ret = [dot(y.T, x.T)]
+        ret = [dot_op(y.mT, x.mT)]
 
         # Copy over stack trace to output from result of dot-product
         copy_stack_trace(node.inputs[0], ret)

pytensor/tensor/rewriting/subtensor_lift.py

@@ -5,7 +5,7 @@
 
 from pytensor import Variable
 from pytensor.compile import optdb
-from pytensor.graph import Constant, FunctionGraph, node_rewriter
+from pytensor.graph import Constant, FunctionGraph, node_rewriter, vectorize_graph
 from pytensor.graph.rewriting.basic import NodeRewriter, copy_stack_trace
 from pytensor.npy_2_compat import normalize_axis_index, normalize_axis_tuple
 from pytensor.scalar import basic as ps
@@ -119,21 +119,43 @@ def local_subtensor_of_dot(fgraph, node):
     the remaining entries of ``idxs`` (if any), modified to skip the
     second-to-last dimension of ``B`` (because dot sums over this dimension).
     """
-    if not isinstance(node.op, Subtensor):
-        return
-    if not (node.inputs[0].owner and isinstance(node.inputs[0].owner.op, Dot)):
+    x, *idx_vars = node.inputs
+    if not (
+        x.owner is not None
+        and (
+            isinstance(x.owner.op, Dot)
+            or (
+                isinstance(x.owner.op, Blockwise)
+                and isinstance(x.owner.op.core_op, Dot)
+            )
+        )
+    ):
         return
     # If there is other node that use the outputs of the dot
     # We don't want to compute twice the sub part.
-    if len(fgraph.clients[node.inputs[0]]) > 1:
+    if len(fgraph.clients[x]) > 1:
         return
 
-    a = node.inputs[0].owner.inputs[0]
-    b = node.inputs[0].owner.inputs[1]
+    a = x.owner.inputs[0]
+    b = x.owner.inputs[1]
+    idx_list = indices_from_subtensor(idx_vars, node.op.idx_list)
 
-    idx_list = get_idx_list(node.inputs, node.op.idx_list)
+    batch_ndim = (
+        x.owner.op.batch_ndim(x.owner) if isinstance(x.owner.op, Blockwise) else 0
+    )
+
+    if batch_ndim:
+        batch_idx_list, idx_list = idx_list[:batch_ndim], idx_list[batch_ndim:]
+        if not idx_list:
+            # Indexing only over batch dimensions of Blockwise, that can be handled by another rewrite
+            return None
+        # We perform the rest of the rewrite on dummy a, b that correspond to the core case
+        a = a.type.clone(shape=a.type.shape[batch_ndim:])()
+        b = b.type.clone(shape=b.type.shape[batch_ndim:])()
 
-    num_a_indices = min(a.ndim - 1, len(idx_list))
+    a_ndim = a.ndim
+    b_ndim = b.ndim
+    num_a_indices = min(a_ndim - 1, len(idx_list))
     a_indices = idx_list[:num_a_indices]
     b_indices = idx_list[num_a_indices:]
 
@@ -142,26 +164,22 @@ def local_subtensor_of_dot(fgraph, node):
     # This wasn't necessary for a, because we just omitted the last index.
     # We skip this if b.ndim = 1, since then we just want b_sub = b, not b_sub = b[:]
     # (dot also handles b.ndim < 2 as a special case)
-    if b.ndim > 1 and len(b_indices) >= b.ndim - 1:
+    if b_ndim > 1 and len(b_indices) >= b_ndim - 1:
         b_indices = (
-            b_indices[: b.ndim - 2]
+            b_indices[: b_ndim - 2]
             + (slice(None, None, None),)
-            + b_indices[b.ndim - 2 :]
+            + b_indices[b_ndim - 2 :]
         )
 
-    a_sub = a.__getitem__(tuple(a_indices))
-    b_sub = b.__getitem__(tuple(b_indices)) if b_indices else b
+    a_sub = a[tuple(a_indices)]
+    b_sub = b[tuple(b_indices)] if b_indices else b
+    r = dot(a_sub, b_sub)
 
-    # Copy over previous output stacktrace to a_sub and b_sub,
-    # because an error in the subtensor operation (e.g. an index error)
-    # on either a or b must correspond to an error in the
-    # subtensor operation on their dot product.
-    copy_stack_trace(node.outputs[0], [a_sub, b_sub])
+    if batch_ndim:
+        # Replace dummy inputs by the original batch ones
+        r = vectorize_graph(r, replace={a: x.owner.inputs[0], b: x.owner.inputs[1]})
+        r = r[tuple(batch_idx_list)]
 
-    # Copy over previous output stacktrace and previous dot product stacktrace,
-    # because an error here may correspond to an either in either the original
-    # dot product, or in the dot product after the subtensor operation.
-    r = dot(a_sub, b_sub)
     copy_stack_trace([node.outputs[0], node.inputs[0]], r)
 
     return [r]