fix(imperative/opr): fix apply_on_var_node for broadcast

GitOrigin-RevId: 686fff4f73

imperative/python/test/unit/functional/test_tensor.py

@@ -309,12 +309,17 @@ def test_broadcast():
     output2_shape = (20, 10, 20)
     data2 = np.random.random(input2_shape).astype(np.float32)
 
+    input3_shape = (10, 10)
+    output3_shape = (10, 10)
+    data3 = np.random.random(input3_shape).astype(np.float32)
+
     def compare_fn(x, y):
         assert x.shape[0] == y
 
     cases = [
         {"input": [data1, output1_shape], "output": output1_shape},
         {"input": [data2, output2_shape], "output": output2_shape},
+        {"input": [data3, output3_shape], "output": output3_shape},
     ]
     opr_test(cases, F.broadcast_to, compare_fn=compare_fn)
 

imperative/src/impl/ops/broadcast.cpp

@@ -24,14 +24,14 @@ std::shared_ptr<OpDef> make_from_op_node(cg::OperatorNodeBase* node_) {
     return Broadcast::make();
 }
 
-cg::OperatorNodeBase* apply_on_var_node(
+auto apply_on_var_node(
         const OpDef& def,
         const VarNodeArray& inputs) {
     auto&& op = def.cast_final_safe<Broadcast>();
     size_t nr_inp = inputs.size();
     mgb_assert(nr_inp == 2, "Broadcast expects 2 inputs; got %lu actually", nr_inp);
     OperatorNodeConfig config{op.make_name()};
-    return opr::Broadcast::make(inputs[0], inputs[1], config).node()->owner_opr();
+    return opr::Broadcast::make(inputs[0], inputs[1], config);
 }
 
 bool valid_broadcast(const TensorShape& src_shape,