Implement Cast in PyTorch backend

pytensor/link/pytorch/dispatch/scalar.py

@@ -2,6 +2,7 @@
 
 from pytensor.link.pytorch.dispatch.basic import pytorch_funcify
 from pytensor.scalar.basic import (
+    Cast,
     ScalarOp,
 )
 
@@ -38,3 +39,13 @@ def pytorch_func(*args):
             )
 
     return pytorch_func
+
+
+@pytorch_funcify.register(Cast)
+def pytorch_funcify_Cast(op: Cast, node, **kwargs):
+    dtype = getattr(torch, op.o_type.dtype)
+
+    def cast(x):
+        return x.to(dtype=dtype)
+
+    return cast

tests/link/pytorch/test_elemwise.py

@@ -11,6 +11,9 @@
 from tests.link.pytorch.test_basic import compare_pytorch_and_py
 
 
+torch = pytest.importorskip("torch")
+
+
 def test_pytorch_Dimshuffle():
     a_pt = matrix("a")
 
@@ -143,3 +146,13 @@ def test_softmax_grad(axis):
     out = SoftmaxGrad(axis=axis)(dy, sm)
     fgraph = FunctionGraph([dy, sm], [out])
     compare_pytorch_and_py(fgraph, [dy_value, sm_value])
+
+
+def test_cast():
+    x = matrix("x", dtype="float32")
+    out = pt.cast(x, "int32")
+    fgraph = FunctionGraph([x], [out])
+    _, [res] = compare_pytorch_and_py(
+        fgraph, [np.arange(6, dtype="float32").reshape(2, 3)]
+    )
+    assert res.dtype == torch.int32