Added PyTorch clip dispatch (#1797)

pytensor/link/pytorch/dispatch/scalar.py

@@ -5,6 +5,7 @@ import torch
 from pytensor.link.pytorch.dispatch.basic import pytorch_funcify
 from pytensor.scalar.basic import (
     Cast,
+    Clip,
     Invert,
     ScalarOp,
 )
@@ -71,6 +72,14 @@ def pytorch_funcify_Softplus(op, node, **kwargs):
     return torch.nn.Softplus()
 
 
+@pytorch_funcify.register(Clip)
+def pytorch_funcify_Clip(op, node, **kwargs):
+    def clip(x, min_val, max_val):
+        return torch.where(x < min_val, min_val, torch.where(x > max_val, max_val, x))
+
+    return clip
+
+
 @pytorch_funcify.register(ScalarLoop)
 def pytorch_funicify_ScalarLoop(op, node, **kwargs):
     update = pytorch_funcify(op.fgraph, **kwargs)

tests/link/pytorch/test_elemwise.py

@@ -151,6 +151,19 @@ def test_cast():
     assert res.dtype == np.int32
 
 
+@pytest.mark.parametrize(
+    "x_val, min_val, max_val",
+    [
+        (np.array([5.0], dtype=config.floatX), 0.0, 10.0),
+        (np.array([-5.0], dtype=config.floatX), 0.0, 10.0),
+    ],
+)
+def test_clip(x_val, min_val, max_val):
+    x = pt.tensor("x", shape=x_val.shape, dtype=config.floatX)
+    out = pt.clip(x, min_val, max_val)
+    compare_pytorch_and_py([x], [out], [x_val])
+
+
 def test_vmap_elemwise():
     from pytensor.link.pytorch.dispatch.basic import pytorch_funcify