Add torch implementation of IfElse

pytensor/link/pytorch/dispatch/basic.py

@@ -6,6 +6,7 @@
 
 from pytensor.compile.ops import DeepCopyOp
 from pytensor.graph.fg import FunctionGraph
+from pytensor.ifelse import IfElse
 from pytensor.link.utils import fgraph_to_python
 from pytensor.raise_op import CheckAndRaise
 from pytensor.tensor.basic import (
@@ -150,6 +151,19 @@
     return makevector
 
 
+@pytorch_funcify.register(IfElse)
+def pytorch_funcify_IfElse(op, **kwargs):
+    n_outs = op.n_outs
+
+    def ifelse(cond, *true_and_false, n_outs=n_outs):
+        if cond:
+            return torch.stack(true_and_false[:n_outs])
+        else:
+            return torch.stack(true_and_false[n_outs:])
+
+    return ifelse
+
+
 @pytorch_funcify.register(TensorFromScalar)
 def pytorch_funcify_TensorFromScalar(op, **kwargs):
     def tensorfromscalar(x):

tests/link/pytorch/test_basic.py

@@ -12,6 +12,7 @@
 from pytensor.graph.basic import Apply
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.op import Op
+from pytensor.ifelse import ifelse
 from pytensor.raise_op import CheckAndRaise
 from pytensor.tensor import alloc, arange, as_tensor, empty, eye
 from pytensor.tensor.type import matrix, scalar, vector
@@ -301,3 +302,20 @@ def test_pytorch_MakeVector():
     x_fg = FunctionGraph([], [x])
 
     compare_pytorch_and_py(x_fg, [])
+
+
+def test_pytorch_ifelse():
+    p1_vals = np.r_[1, 2, 3]
+    p2_vals = np.r_[-1, -2, -3]
+
+    a = scalar("a")
+    x = ifelse(a < 0.5, tuple(np.r_[p1_vals, p2_vals]), tuple(np.r_[p2_vals, p1_vals]))
+    x_fg = FunctionGraph([a], x)
+
+    compare_pytorch_and_py(x_fg, np.array([0.2], dtype=config.floatX))
+
+    a = scalar("a")
+    x = ifelse(a < 0.4, tuple(np.r_[p1_vals, p2_vals]), tuple(np.r_[p2_vals, p1_vals]))
+    x_fg = FunctionGraph([a], x)
+
+    compare_pytorch_and_py(x_fg, np.array([0.5], dtype=config.floatX))