Add rewrite for `softplus(log(x)) -> log1p(x)`

pytensor/tensor/rewriting/math.py

@@ -576,7 +576,7 @@ def local_log_sqrt(fgraph, node):
 
 
 @register_specialize
-@node_rewriter([exp, expm1])
+@node_rewriter([exp, expm1, softplus])
 def local_exp_log_nan_switch(fgraph, node):
     # Rewrites of the kind exp(log...(x)) that require a `nan` switch
     x = node.inputs[0]
@@ -629,6 +629,13 @@ def local_exp_log_nan_switch(fgraph, node):
         new_out = switch(le(x, 0), neg(exp(x)), np.asarray(np.nan, old_out.dtype))
         return [new_out]
 
+    # Case for softplus(log(x)) -> log1p(x)
+    if isinstance(prev_op, ps.Log) and isinstance(node_op, ps_math.Softplus):
+        x = x.owner.inputs[0]
+        old_out = node.outputs[0]
+        new_out = switch(ge(x, 0), log1p(x), np.asarray(np.nan, old_out.dtype))
+        return [new_out]
+
 
 @register_canonicalize
 @register_specialize

tests/tensor/rewriting/test_math.py

@@ -1968,6 +1968,27 @@ class TestExpLog:
             decimal=6,
         )
 
+    def test_softplus_log(self):
+        # softplus(log(x)) -> log1p(x)
+        data_valid = np.random.random((4, 3)).astype("float32") * 2
+        data_valid[0, 0] = 0  # edge case
+        data_invalid = data_valid - 2
+
+        x = fmatrix()
+        f = function([x], softplus(log(x)), mode=self.mode)
+        graph = f.maker.fgraph.toposort()
+        ops_graph = [
+            node
+            for node in graph
+            if isinstance(node.op, Elemwise)
+            and isinstance(node.op.scalar_op, ps.Log | ps.Exp | ps.Softplus)
+        ]
+        assert len(ops_graph) == 0
+
+        expected = np.log1p(data_valid)
+        np.testing.assert_almost_equal(f(data_valid), expected)
+        assert np.all(np.isnan(f(data_invalid)))
+
     @pytest.mark.parametrize(
         ["nested_expression", "expected_switches"],
         [