Rewrite concatenate([x, x]) as repeat(x, 2)

pytensor/tensor/rewriting/basic.py

@@ -82,7 +82,7 @@
 )
 from pytensor.tensor.elemwise import DimShuffle, Elemwise
 from pytensor.tensor.exceptions import NotScalarConstantError
-from pytensor.tensor.extra_ops import broadcast_arrays
+from pytensor.tensor.extra_ops import broadcast_arrays, repeat
 from pytensor.tensor.math import Sum, add, eq, variadic_add
 from pytensor.tensor.shape import Shape_i, shape_padleft
 from pytensor.tensor.type import DenseTensorType, TensorType
@@ -910,6 +910,38 @@ def local_join_make_vector(fgraph, node):
         return [ret]
 
 
+@register_canonicalize
+@node_rewriter([Join])
+def local_join_to_repeat(fgraph, node):
+    """Join(axis, x, x, x, ...) -> repeat(x, n, axis)
+
+    When the same tensor is concatenated multiple times,
+    replace with a single repeat operation which is more efficient.
+
+    Examples
+    --------
+    concatenate([x, x, x], axis=0) -> repeat(x, 3, axis=0)
+    """
+    # Extract axis and the tensors being joined
+    axis, *tensors = node.inputs
+
+    # Need at least 2 tensors to consider optimization
+    if len(tensors) <= 1:
+        return
+
+    # Check if all tensors are identical
+    if not all(t == tensors[0] for t in tensors[1:]):
+        return
+
+    # Replace with repeat operation
+    result = repeat(tensors[0], len(tensors), axis)
+
+    # Preserve debugging information
+    copy_stack_trace(node.outputs[0], result)
+
+    return [result]
+
+
 @register_specialize
 @register_canonicalize
 @register_useless

tests/tensor/rewriting/test_basic.py

@@ -35,6 +35,7 @@
     tile,
 )
 from pytensor.tensor.elemwise import DimShuffle, Elemwise
+from pytensor.tensor.extra_ops import Repeat
 from pytensor.tensor.math import (
     add,
     bitwise_and,
@@ -1247,6 +1248,86 @@ def test_local_join_1():
     assert f.maker.fgraph.outputs[0].dtype == config.floatX
 
 
+def test_local_join_to_repeat():
+    """Test that Join(axis, x, x, ...) gets rewritten to repeat(x, n, axis)"""
+
+    # Test with vector - concatenate same vector 3 times along axis 0
+    x = vector("x")
+    s = join(0, x, x, x)
+    f = function([x], s, mode=rewrite_mode)
+
+    # Check numerical correctness
+    test_val = np.array([1.0, 2.0, 3.0], dtype=config.floatX)
+    result = f(test_val)
+    expected = np.array(
+        [1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0], dtype=config.floatX
+    )
+    assert np.allclose(result, expected)
+
+    # Check that Join was replaced with Repeat
+    ops = f.maker.fgraph.toposort()
+    assert len([n for n in ops if isinstance(n.op, Join)]) == 0
+    assert len([n for n in ops if isinstance(n.op, Repeat)]) == 1
+
+    # Test with matrix - concatenate same matrix along axis 0
+    a = matrix("a")
+    s = join(0, a, a, a, a)
+    f = function([a], s, mode=rewrite_mode)
+
+    test_mat = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=config.floatX)
+    result = f(test_mat)
+    expected = np.vstack([test_mat, test_mat, test_mat, test_mat])
+    assert np.allclose(result, expected)
+
+    # Check optimization applied
+    ops = f.maker.fgraph.toposort()
+    assert len([n for n in ops if isinstance(n.op, Join)]) == 0
+    assert len([n for n in ops if isinstance(n.op, Repeat)]) == 1
+
+    # Test with matrix - concatenate along axis 1
+    s = join(1, a, a)
+    f = function([a], s, mode=rewrite_mode)
+
+    result = f(test_mat)
+    expected = np.hstack([test_mat, test_mat])
+    assert np.allclose(result, expected)
+
+    # Check optimization applied
+    ops = f.maker.fgraph.toposort()
+    assert len([n for n in ops if isinstance(n.op, Join)]) == 0
+    assert len([n for n in ops if isinstance(n.op, Repeat)]) == 1
+
+    # Test that it does NOT apply when tensors are different
+    b = matrix("b")
+    s = join(0, a, b)
+    f = function([a, b], s, mode=rewrite_mode)
+
+    test_mat1 = np.array([[1.0, 2.0]], dtype=config.floatX)
+    test_mat2 = np.array([[3.0, 4.0]], dtype=config.floatX)
+    result = f(test_mat1, test_mat2)
+    expected = np.vstack([test_mat1, test_mat2])
+    assert np.allclose(result, expected)
+
+    # Join should still be present (not optimized to Repeat)
+    ops = f.maker.fgraph.toposort()
+    assert len([n for n in ops if isinstance(n.op, Join)]) == 1
+    assert len([n for n in ops if isinstance(n.op, Repeat)]) == 0
+
+    # Test with 5 repetitions to ensure it works with larger counts
+    s = join(0, x, x, x, x, x)
+    f = function([x], s, mode=rewrite_mode)
+
+    test_val = np.array([1.0, 2.0], dtype=config.floatX)
+    result = f(test_val)
+    expected = np.tile(test_val, 5)
+    assert np.allclose(result, expected)
+
+    # Check optimization applied
+    ops = f.maker.fgraph.toposort()
+    assert len([n for n in ops if isinstance(n.op, Join)]) == 0
+    assert len([n for n in ops if isinstance(n.op, Repeat)]) == 1
+
+
 def test_local_join_empty():
     # Vector case
     empty_vec = np.asarray([], dtype=config.floatX)