diff --git a/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc b/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc
index 5a16cda14eb4ecd9496402d7ad6b875794cffd28..9f70c829e1fb5ea8c13c45bdacbc408d8cc40173 100755
--- a/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc
+++ b/paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc
@@ -115,7 +115,21 @@ Conv2DTransposeBiasFusePass::Conv2DTransposeBiasFusePass() {
       .IsStringIn({"EXPLICIT", "SAME", "VALID"})
       .End()
       .AddAttr("data_format")
-      .IsStringIn({"NCHW", "NHWC", "AnyLayout"})
+      .IsStringIn({"NCHW"})
+      .End();
+
+  AddOpCompat(OpCompat("elementwise_add"))
+      .AddInput("X")
+      .IsTensor()
+      .End()
+      .AddInput("Y")
+      .IsTensor()
+      .End()
+      .AddOutput("Out")
+      .IsTensor()
+      .End()
+      .AddAttr("axis")
+      .IsIntIn({1})
       .End();
 }
 
diff --git a/python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt b/python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
index e2002765b1fd9a130ee86ddc8ee0f4065d0db35c..56bc97dea43f75f508c3e1b81bb46bd49f03deee 100755
--- a/python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
+++ b/python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
@@ -95,5 +95,6 @@ if (WITH_MKLDNN)
   set_tests_properties(test_conv_act_mkldnn_fuse_pass PROPERTIES TIMEOUT 120)
   set_tests_properties(test_conv_transpose_eltwiseadd_bn_fuse_pass PROPERTIES TIMEOUT 250)
   set_tests_properties(test_conv_transpose_bn_fuse_pass PROPERTIES TIMEOUT 300)
+  set_tests_properties(test_mkldnn_conv_transpose_bias_fuse_pass PROPERTIES TIMEOUT 100)
 endif()
 endif()
diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_conv_transpose_bias_fuse_pass.py b/python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_conv_transpose_bias_fuse_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..5df7cb8d8cec35fd62c373e46d5b799bea46a2da
--- /dev/null
+++ b/python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_conv_transpose_bias_fuse_pass.py
@@ -0,0 +1,117 @@
+# 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 auto_scan_test import PassAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig, OpConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+import unittest
+
+import hypothesis
+from hypothesis import given, settings, seed, example, assume
+import hypothesis.strategies as st
+
+
+class TestConvTransposeMkldnnFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # If the problem has been fixed, the judgment 
+        # needs to be deleted!!!
+        if attrs[0]['data_format'] == "NHWC":
+            return False
+
+        return True
+
+    def sample_program_config(self, draw):
+        data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
+        dilations = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
+        groups = draw(st.sampled_from([1, 2, 4, 8]))
+        paddings = draw(st.sampled_from([[0, 3], [1, 2, 3, 4]]))
+        strides = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        axis = draw(st.sampled_from([1]))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+
+        def generate_input():
+            if data_format == "NCHW":
+                return np.random.random(
+                    [batch_size, 16, 64, 64]).astype(np.float32)
+            else:
+                return np.random.random(
+                    [batch_size, 64, 64, 16]).astype(np.float32)
+
+        def generate_weight1():
+            return np.random.random([16, 16, 3, 3]).astype(np.float32)
+
+        def generate_weight2():
+            return np.random.random([16 * groups]).astype(np.float32)
+
+        conv2d_op = OpConfig(
+            type="conv2d_transpose",
+            inputs={"Input": ["input_data"],
+                    "Filter": ["conv2d_weight"]},
+            outputs={"Output": ["conv_output"]},
+            attrs={
+                "data_format": data_format,
+                "dilations": dilations,
+                "padding_algorithm": padding_algorithm,
+                "groups": groups,
+                "paddings": paddings,
+                "strides": strides,
+                "output_size": [],
+                "output_padding": [],
+                "is_test": True
+            })
+
+        elt_op = OpConfig(
+            type="elementwise_add",
+            inputs={"X": ["conv_output"],
+                    "Y": ["elementwise_weight"]},
+            outputs={"Out": ["elementwise_output"]},
+            attrs={'axis': axis})
+
+        model_net = [conv2d_op, elt_op]
+
+        program_config = ProgramConfig(
+            ops=model_net,
+            weights={
+                "conv2d_weight":
+                TensorConfig(data_gen=partial(generate_weight1)),
+                "elementwise_weight":
+                TensorConfig(data_gen=partial(generate_weight2))
+            },
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input))
+            },
+            outputs=["elementwise_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ['conv2d_transpose'], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False, passes=["conv_transpose_bias_mkldnn_fuse_pass"])
+
+
+if __name__ == "__main__":
+    unittest.main()