Fix

Author: co63oc

test/ir/inference/CMakeLists.txt

@@ -311,7 +311,7 @@ elseif(WITH_ONEDNN)
       test_mkldnn_conv3d_op
       test_mkldnn_depthwise_conv_pass
       test_mkldnn_shape_op
-      test_mkldnn_shuffle_channel_op)
+      test_onednn_shuffle_channel_op)
   foreach(target ${PIR_COVERAGE_MKLDNN_TESTS})
     py_test_modules(${target}_pir MODULES ${target} ENVS FLAGS_enable_pir_api=1)
     set_tests_properties(${target} PROPERTIES LABELS "RUN_TYPE=INFER")
@@ -335,7 +335,7 @@ elseif(WITH_ONEDNN)
   endforeach()
 
   set_tests_properties(test_mkldnn_shape_op_pir PROPERTIES TIMEOUT 300)
-  set_tests_properties(test_mkldnn_shuffle_channel_op_pir PROPERTIES TIMEOUT
+  set_tests_properties(test_onednn_shuffle_channel_op_pir PROPERTIES TIMEOUT
                                                                      300)
   set_tests_properties(test_onednn_conv_bias_fuse_pass_pir PROPERTIES TIMEOUT
                                                                       300)

test/ir/inference/test_onednn_shuffle_channel_op.py

@@ -0,0 +1,66 @@
+# Copyright (c) 2022 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.
+
+import unittest
+from functools import partial
+
+import hypothesis.strategies as st
+import numpy as np
+from auto_scan_test import OnednnAutoScanTest
+from hypothesis import given
+from program_config import OpConfig, ProgramConfig, TensorConfig
+
+
+class TestMKLDNNShuffleChannelOp(OnednnAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self, *args, **kwargs):
+        def generate_input(*args, **kwargs):
+            return np.random.random(kwargs['in_shape']).astype(np.float32)
+
+        shuffle_channel_op = OpConfig(
+            type="shuffle_channel",
+            inputs={"X": ["input_data"]},
+            outputs={"Out": ["output_data"]},
+            attrs={"group": kwargs['group']},
+        )
+
+        program_config = ProgramConfig(
+            ops=[shuffle_channel_op],
+            weights={},
+            inputs={
+                "input_data": TensorConfig(
+                    data_gen=partial(generate_input, *args, **kwargs)
+                ),
+            },
+            outputs=["output_data"],
+        )
+
+        yield program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_onednn=True)
+        yield config, (1e-5, 1e-5)
+
+    @given(
+        group=st.sampled_from([1, 2, 8, 32, 128]),
+        in_shape=st.sampled_from([[5, 512, 2, 3], [2, 256, 5, 4]]),
+    )
+    def test(self, *args, **kwargs):
+        self.run_test(quant=False, *args, **kwargs)
+
+
+if __name__ == "__main__":
+    unittest.main()