Rewrite away blockwise Subtensor in gradient of Blockwise(Conv1d)

pytensor/tensor/rewriting/blockwise.py

@@ -14,7 +14,12 @@ from pytensor.tensor.rewriting.basic import (
     register_stabilize,
 )
 from pytensor.tensor.shape import Reshape
-from pytensor.tensor.subtensor import AdvancedIncSubtensor, AdvancedSubtensor, Subtensor
+from pytensor.tensor.subtensor import (
+    AdvancedIncSubtensor,
+    AdvancedSubtensor,
+    Subtensor,
+    indices_from_subtensor,
+)
 
 
 @node_rewriter([Blockwise])
@@ -216,9 +221,9 @@ def local_blockwise_reshape(fgraph, node):
 
     Reshape is tricky to vectorize eagerly, because a graph like
     `x.reshape([x.shape[0] * x.shape[1], -1])` has many operations
-    that must be vectorized before we arrize at the reshape operation.
+    that must be vectorized before we arrive at the reshape operation.
 
-    For the square Reshape case, we must wait for all the intemediate
+    For the square Reshape case, we must wait for all the intermediate
     operations to be lifted as Allocs
     """
     if not isinstance(node.op.core_op, Reshape):
@@ -234,6 +239,29 @@ def local_blockwise_reshape(fgraph, node):
         return [new_out]
 
 
+@register_stabilize
+@register_specialize
+@node_rewriter([Blockwise])
+def local_blockwise_of_subtensor(fgraph, node):
+    """Rewrite Blockwise of Subtensor, where the only batch input is the indexed tensor.
+
+    Blockwise(Subtensor{a: b})(x, a, b) -> x[:, a:b] when x has one batch dimension, and a/b none
+    """
+    if not isinstance(node.op.core_op, Subtensor):
+        return
+
+    x, *idxs = node.inputs
+    if not all(all(idx.type.broadcastable) for idx in idxs):
+        return
+
+    core_idxs = indices_from_subtensor(
+        [idx.squeeze() for idx in idxs], node.op.core_op.idx_list
+    )
+    # Add empty slices for the batch dims
+    none_slices = (slice(None),) * node.op.batch_ndim(node)
+    return [x[(*none_slices, *core_idxs)]]
+
+
 @node_rewriter(tracks=[Blockwise], inplace=True)
 def blockwise_inplace(fgraph, node):
     blockwise_op = node.op

tests/tensor/signal/test_conv.py

@@ -4,9 +4,11 @@ import numpy as np
 import pytest
 from scipy.signal import convolve as scipy_convolve
 
-from pytensor import config, function
+from pytensor import config, function, grad
+from pytensor.graph import ancestors, rewrite_graph
 from pytensor.tensor import matrix, vector
-from pytensor.tensor.signal.conv import convolve1d
+from pytensor.tensor.blockwise import Blockwise
+from pytensor.tensor.signal.conv import Conv1d, convolve1d
 from tests import unittest_tools as utt
 
 
@@ -60,3 +62,23 @@ def test_convolve1d_batch_same():
 
     res = out.eval({x: x_test, y: y_test})
     assert res.shape == (2, 8)
+
+
+@pytest.mark.parametrize("mode", ("full", "valid", "same"))
+def test_convolve1d_batch_graph(mode):
+    """Test that we don't have slow Blockwise Subtensors in graph of a batched convolve1d"""
+    x = matrix("x")
+    y = matrix("y")
+    out = convolve1d(x, y, mode=mode)
+    grads = grad(out.sum(), wrt=[x, y])
+    final_grads = rewrite_graph(
+        grads, include=("ShapeOpt", "canonicalize", "stabilize", "specialize")
+    )
+
+    blockwise_nodes = [
+        var.owner
+        for var in ancestors(final_grads)
+        if var.owner is not None and isinstance(var.owner.op, Blockwise)
+    ]
+    # Check any Blockwise are just Conv1d
+    assert all(isinstance(node.op.core_op, Conv1d) for node in blockwise_nodes)