diff --git a/test/ir/inference/test_trt_convert_reshape.py b/test/ir/inference/test_trt_convert_reshape.py
index 3f88b39003bb9dea6cf468b88d9cb1637d4d2b6b..c30d973651bad125911aad42dc0bc43749d32c03 100644
--- a/test/ir/inference/test_trt_convert_reshape.py
+++ b/test/ir/inference/test_trt_convert_reshape.py
@@ -431,5 +431,99 @@ class TrtConvertReshapeTest3(TrtLayerAutoScanTest):
         self.run_test()
 
 
+class TrtConvertReshapeZeroDimsTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            if self.dims > 0:
+                self.input_shape = [1] * self.dims
+                return np.random.random(self.input_shape).astype(np.float32)
+            elif self.dims == 0:
+                self.input_shape = []
+                return np.random.random([]).astype(np.float32)
+
+        for dims in [0, 1, 2, 3]:
+            for shape in [
+                [],
+                [1, 1],
+            ]:
+                dics = [
+                    {
+                        "shape": shape,
+                    },
+                ]
+                self.dims = dims
+                dics_intput = [{"X": ["reshape_input"]}]
+
+                ops_config = [
+                    {
+                        "op_type": "reshape",
+                        "op_inputs": dics_intput[0],
+                        "op_outputs": {"Out": ["reshape_out"]},
+                        "op_attrs": dics[0],
+                    }
+                ]
+                ops = self.generate_op_config(ops_config)
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "reshape_input": TensorConfig(
+                            data_gen=partial(generate_input1, dics)
+                        )
+                    },
+                    outputs=["reshape_out"],
+                )
+
+                yield program_config
+
+    def sample_predictor_configs(
+        self, program_config
+    ) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            self.dynamic_shape.min_input_shape = {
+                "reshape_input": self.input_shape
+            }
+            self.dynamic_shape.max_input_shape = {
+                "reshape_input": self.input_shape
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "reshape_input": self.input_shape
+            }
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            # only test dynamic shape mode
+            return 1, 2
+
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True
+        ), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True
+        ), 1e-3
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
 if __name__ == "__main__":
     unittest.main()