diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_softmax.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_softmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..e539bd9a563004ce793d01a64711584d64570663
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_softmax.py
@@ -0,0 +1,146 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+
+
+class TrtConvertSoftmaxTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        outputs = program_config.outputs
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        #The input dimension should be less than or equal to the set axis.
+        if len(inputs['softmax_input'].shape) <= attrs[0]['axis']:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.ones([batch, 3, 24, 24]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([batch, 3, 24]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([batch, 32]).astype(np.float32)
+
+        for dims in [2, 3, 4]:
+            for batch in [1, 2, 4]:
+                for axis in [-1, 0, 1, 2, 3]:
+                    self.dims = dims
+                    dics = [{"axis": axis}, {}]
+                    ops_config = [{
+                        "op_type": "softmax",
+                        "op_inputs": {
+                            "X": ["softmax_input"]
+                        },
+                        "op_outputs": {
+                            "Out": ["softmax_out"]
+                        },
+                        "op_attrs": dics[0]
+                    }]
+                    ops = self.generate_op_config(ops_config)
+                    program_config = ProgramConfig(
+                        ops=ops,
+                        weights={},
+                        inputs={
+                            "softmax_input": TensorConfig(data_gen=partial(
+                                generate_input1, dics, batch))
+                        },
+                        outputs=["softmax_out"])
+
+                    yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "softmax_input": [1, 3, 24, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "softmax_input": [4, 3, 48, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "softmax_input": [1, 3, 24, 48]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "softmax_input": [1, 3, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "softmax_input": [4, 3, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "softmax_input": [1, 3, 48]
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"softmax_input": [1, 32]}
+                self.dynamic_shape.max_input_shape = {"softmax_input": [4, 64]}
+                self.dynamic_shape.opt_input_shape = {"softmax_input": [1, 32]}
+
+        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):
+            return 1, 2
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        # for static_shape
+        clear_dynamic_shape()
+        if attrs[0]['axis'] == 0:
+            pass
+        else:
+            self.trt_param.precision = paddle_infer.PrecisionType.Float32
+            yield self.create_inference_config(), generate_trt_nodes_num(
+                attrs, False), 1e-5
+            self.trt_param.precision = paddle_infer.PrecisionType.Half
+            yield self.create_inference_config(), generate_trt_nodes_num(
+                attrs, False), 1e-5
+
+        # 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-5
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()