Simplify string expression for Constant Variables

pytensor/graph/basic.py

@@ -763,13 +763,20 @@ class Constant(AtomicVariable[_TypeType]):
         return (self.type, self.data)
 
     def __str__(self):
-        if self.name is not None:
-            return self.name
-        else:
-            name = str(self.data)
-            if len(name) > 20:
-                name = name[:10] + "..." + name[-10:]
-            return f"{type(self).__name__}{{{name}}}"
+        data_str = str(self.data)
+        if len(data_str) > 20:
+            data_str = data_str[:10].strip() + " ... " + data_str[-10:].strip()
+
+        if self.name is None:
+            return data_str
+
+        return f"{self.name}{{{data_str}}}"
+
+    def __repr__(self):
+        data_str = repr(self.data)
+        if len(data_str) > 20:
+            data_str = data_str[:10].strip() + " ... " + data_str[-10:].strip()
+        return f"{type(self).__name__}({repr(self.type)}, data={data_str})"
 
     def clone(self, **kwargs):
         return self

pytensor/tensor/var.py

@@ -1023,21 +1023,6 @@ class TensorConstant(TensorVariable, Constant[_TensorTypeType]):
 
         Constant.__init__(self, new_type, data, name)
 
-    def __str__(self):
-        unique_val = get_unique_value(self)
-        if unique_val is not None:
-            val = f"{self.data.shape} of {unique_val}"
-        else:
-            val = f"{self.data}"
-        if len(val) > 20:
-            val = val[:10].strip() + " ... " + val[-10:].strip()
-
-        if self.name is not None:
-            name = self.name
-        else:
-            name = "TensorConstant"
-        return f"{name}{{{val}}}"
-
     def signature(self):
         return TensorConstantSignature((self.type, self.data))
 

tests/graph/rewriting/test_basic.py

@@ -166,7 +166,7 @@ class TestPatternNodeRewriter:
         e = op1(op1(x, y), y)
         g = FunctionGraph([y], [e])
         OpKeyPatternNodeRewriter((op1, z, "1"), (op2, "1", z)).rewrite(g)
-        assert str(g) == "FunctionGraph(Op1(Op2(y, z), y))"
+        assert str(g) == "FunctionGraph(Op1(Op2(y, z{2}), y))"
 
     def test_constraints(self):
         x, y, z = MyVariable("x"), MyVariable("y"), MyVariable("z")

tests/tensor/test_var.py

@@ -213,7 +213,7 @@ def test_print_constant():
     c = pytensor.tensor.constant(1, name="const")
     assert str(c) == "const{1}"
     d = pytensor.tensor.constant(1)
-    assert str(d) == "TensorConstant{1}"
+    assert str(d) == "1"
 
 
 @pytest.mark.parametrize(

tests/test_printing.py

@@ -280,10 +280,10 @@ def test_debugprint():
          │     ├─ AllocEmpty{dtype='float64'} 1
          │     │  └─ Shape_i{0} 0
          │     │     └─ B
-         │     ├─ TensorConstant{1.0}
+         │     ├─ 1.0
          │     ├─ B
          │     ├─ <Vector(float64, shape=(?,))>
-         │     └─ TensorConstant{0.0}
+         │     └─ 0.0
          ├─ D
          └─ A