Vectorize Subtensor without batched indices

Author: ricardoV94

pytensor/tensor/subtensor.py

@@ -13,6 +13,7 @@
 from pytensor.gradient import DisconnectedType
 from pytensor.graph.basic import Apply, Constant, Variable
 from pytensor.graph.op import Op
+from pytensor.graph.replace import _vectorize_node
 from pytensor.graph.type import Type
 from pytensor.graph.utils import MethodNotDefined
 from pytensor.link.c.op import COp
@@ -22,6 +23,7 @@
 from pytensor.scalar.basic import ScalarConstant
 from pytensor.tensor import _get_vector_length, as_tensor_variable, get_vector_length
 from pytensor.tensor.basic import alloc, get_underlying_scalar_constant_value, nonzero
+from pytensor.tensor.blockwise import vectorize_node_fallback
 from pytensor.tensor.elemwise import DimShuffle
 from pytensor.tensor.exceptions import AdvancedIndexingError, NotScalarConstantError
 from pytensor.tensor.math import clip
@@ -1283,6 +1285,21 @@ def _process(self, idxs, op_inputs, pstate):
 pprint.assign(Subtensor, SubtensorPrinter())
 
 
+# TODO: Implement similar vectorize for Inc/SetSubtensor
+@_vectorize_node.register(Subtensor)
+def vectorize_subtensor(op: Subtensor, node, batch_x, *batch_idxs):
+    """Rewrite subtensor with non-batched indexes as another Subtensor with prepended empty slices."""
+
+    # TODO: Vectorize Subtensor with non-slice batched indexes as AdvancedSubtensor
+    if any(batch_inp.type.ndim > 0 for batch_inp in batch_idxs):
+        return vectorize_node_fallback(op, node, batch_x, *batch_idxs)
+
+    old_x, *_ = node.inputs
+    batch_ndims = batch_x.type.ndim - old_x.type.ndim
+    new_idx_list = (slice(None),) * batch_ndims + op.idx_list
+    return Subtensor(new_idx_list).make_node(batch_x, *batch_idxs)
+
+
 def set_subtensor(x, y, inplace=False, tolerate_inplace_aliasing=False):
     """
     Return x with the given subtensor overwritten by y.

tests/tensor/test_subtensor.py

@@ -9,14 +9,16 @@
 import pytensor
 import pytensor.scalar as scal
 import pytensor.tensor.basic as at
+from pytensor import function
 from pytensor.compile import DeepCopyOp, shared
 from pytensor.compile.io import In
 from pytensor.configdefaults import config
 from pytensor.graph.op import get_test_value
 from pytensor.graph.rewriting.utils import is_same_graph
 from pytensor.printing import pprint
 from pytensor.scalar.basic import as_scalar
-from pytensor.tensor import get_vector_length
+from pytensor.tensor import get_vector_length, vectorize
+from pytensor.tensor.blockwise import Blockwise
 from pytensor.tensor.elemwise import DimShuffle
 from pytensor.tensor.math import exp, isinf
 from pytensor.tensor.math import sum as at_sum
@@ -2709,3 +2711,43 @@ def test_static_shapes(x_shape, indices, expected):
     x = at.tensor(dtype="float64", shape=x_shape)
     y = x[indices]
     assert y.type.shape == expected
+
+
+def test_vectorize_subtensor_without_batch_indices():
+    signature = "(t1,t2,t3),()->(t1,t3)"
+
+    def core_fn(x, start):
+        return x[:, start, :]
+
+    x = tensor(shape=(11, 7, 5, 3))
+    start = tensor(shape=(), dtype="int")
+    vectorize_pt = function(
+        [x, start], vectorize(core_fn, signature=signature)(x, start)
+    )
+    assert not any(
+        isinstance(node.op, Blockwise) for node in vectorize_pt.maker.fgraph.apply_nodes
+    )
+    x_test = np.random.normal(size=x.type.shape).astype(x.type.dtype)
+    start_test = np.random.randint(0, x.type.shape[-2])
+    vectorize_np = np.vectorize(core_fn, signature=signature)
+    np.testing.assert_allclose(
+        vectorize_pt(x_test, start_test),
+        vectorize_np(x_test, start_test),
+    )
+
+    # If we vectorize start, we should get a Blockwise that still works
+    x = tensor(shape=(11, 7, 5, 3))
+    start = tensor(shape=(11,), dtype="int")
+    vectorize_pt = function(
+        [x, start], vectorize(core_fn, signature=signature)(x, start)
+    )
+    assert any(
+        isinstance(node.op, Blockwise) for node in vectorize_pt.maker.fgraph.apply_nodes
+    )
+    x_test = np.random.normal(size=x.type.shape).astype(x.type.dtype)
+    start_test = np.random.randint(0, x.type.shape[-2], size=start.type.shape[0])
+    vectorize_np = np.vectorize(core_fn, signature=signature)
+    np.testing.assert_allclose(
+        vectorize_pt(x_test, start_test),
+        vectorize_np(x_test, start_test),
+    )