API(fluid.layers.array_length) error message enhancement (#23547)

paddle/fluid/operators/lod_array_length_op.cc

@@ -60,8 +60,9 @@ CPU and the length of LoDTensorArray should be used as control variables.
 class LoDArrayLengthInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *context) const override {
-    PADDLE_ENFORCE(context->HasInput("X"));
-    PADDLE_ENFORCE(context->HasOutput("Out"));
+    OP_INOUT_CHECK(context->HasInput("X"), "Input", "X", "LDArrayLength");
+    OP_INOUT_CHECK(context->HasOutput("Out"), "Output", "Out",
+                   "LoDArrayLength");
     context->SetOutputDim("Out", {1});
   }
 };

python/paddle/fluid/layers/control_flow.py

@@ -1782,12 +1782,19 @@ def array_length(array):
             #       so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux, 
             #       and '__int64' on Windows. They both represent 64-bit integer variables.
     """
+
     if in_dygraph_mode():
         assert isinstance(
             array,
             list), "The 'array' in array_write must be a list in dygraph mode"
         return len(array)
 
+    if not isinstance(
+            array,
+            Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
+        raise TypeError(
+            "array should be tensor array vairable in array_length Op")
+
     helper = LayerHelper('array_length', **locals())
     tmp = helper.create_variable_for_type_inference(dtype='int64')
     tmp.stop_gradient = True

python/paddle/fluid/tests/unittests/test_lod_array_length_op.py

@@ -18,6 +18,8 @@ import unittest
 import paddle.fluid.layers as layers
 from paddle.fluid.executor import Executor
 import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
 import numpy
 
 
@@ -33,5 +35,14 @@ class TestLoDArrayLength(unittest.TestCase):
         self.assertEqual(11, result[0])
 
 
+class TestLoDArrayLengthOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            #for ci coverage
+            x1 = numpy.random.randn(2, 4).astype('int32')
+
+            self.assertRaises(TypeError, fluid.layers.array_length, array=x1)
+
+
 if __name__ == '__main__':
     unittest.main()