add conv+hard_sigmoid and conv+hard_swish fuse pass ut (#37553)

* add conv+hard_sigmoid fuse pass ut

* update conv_elementwise_add_mkldnn_fuse_pass ut

* update conv_hard_sigmoid_mkldnn_fuse_pass ut

* update conv+hard_sigmoid and conv+hard_swish fuse pass ut

* update ut

* update ut

paddle/fluid/framework/ir/mkldnn/conv_activation_mkldnn_fuse_pass.cc

@@ -157,7 +157,7 @@ ConvActivationFusePass::ConvActivationFusePass() {
       // IsStringIn({"NHWC", "NCHW"}) MobileNetV2 has no this attribute
       .AddAttr("data_format")
       .IsOptional()
-      .IsStringIn({"NHWC", "NCHW", "AnyLayout"})
+      .IsStringIn({"NCHW", "AnyLayout"})
       .End();
 
   AddOpCompat(OpCompat("relu"))

python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt

@@ -98,6 +98,8 @@ 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_hard_sigmoid_fuse_pass PROPERTIES TIMEOUT 300)
+  set_tests_properties(test_mkldnn_conv_hard_swish_fuse_pass PROPERTIES TIMEOUT 300)
   set_tests_properties(test_mkldnn_batch_norm_act_fuse_pass PROPERTIES TIMEOUT 100)
   set_tests_properties(test_mkldnn_conv_transpose_bias_fuse_pass PROPERTIES TIMEOUT 100)
   set_tests_properties(test_conv_eltwiseadd_bn_fuse_pass PROPERTIES TIMEOUT 300)

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_conv_hard_sigmoid_fuse_pass.py

+# 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
+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 TestConvHardSigmoidMkldnnFusePass(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]))
+        paddings = draw(st.sampled_from([[0, 3], [1, 2, 3, 4]]))
+        strides = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        slope = draw(st.floats(min_value=0, max_value=10))
+        offset = draw(st.floats(min_value=0, max_value=10))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+
+        def generate_input():
+            if data_format == "NCHW":
+                return np.random.random(
+                    [batch_size, 48, 64, 64]).astype(np.float32)
+            else:
+                return np.random.random(
+                    [batch_size, 64, 64, 48]).astype(np.float32)
+
+        def generate_weight():
+            return np.random.random(
+                [16, int(48 / groups), 3, 3]).astype(np.float32)
+
+        ops_config = [{
+            "op_type": "conv2d",
+            "op_inputs": {
+                "Input": ["input_data"],
+                "Filter": ["input_weight"]
+            },
+            "op_outputs": {
+                "Output": ["conv_output"]
+            },
+            "op_attrs": {
+                "data_format": data_format,
+                "dilations": dilations,
+                "padding_algorithm": padding_algorithm,
+                "groups": groups,
+                "paddings": paddings,
+                "strides": strides
+            }
+        }, {
+            "op_type": "hard_sigmoid",
+            "op_inputs": {
+                "X": ["conv_output"]
+            },
+            "op_outputs": {
+                "Out": ["sigmoid_output"]
+            },
+            "op_attrs": {
+                "slope": slope,
+                "offset": offset
+            },
+        }]
+
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={
+                "input_weight": TensorConfig(data_gen=partial(generate_weight))
+            },
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input)),
+            },
+            outputs=["sigmoid_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ["conv2d"], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False, passes=["conv_hard_sigmoid_mkldnn_fuse_pass"])
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_conv_hard_swish_fuse_pass.py

+# 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
+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 TestConvHardSwishMkldnnFusePass(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]))
+        paddings = draw(st.sampled_from([[0, 3], [1, 2, 3, 4]]))
+        strides = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        threshold = draw(st.sampled_from([6.0]))
+        scale = draw(st.sampled_from([6.0]))
+        offset = draw(st.sampled_from([3.0]))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+
+        def generate_input():
+            if data_format == "NCHW":
+                return np.random.random(
+                    [batch_size, 48, 64, 64]).astype(np.float32)
+            else:
+                return np.random.random(
+                    [batch_size, 64, 64, 48]).astype(np.float32)
+
+        def generate_weight():
+            return np.random.random(
+                [16, int(48 / groups), 3, 3]).astype(np.float32)
+
+        ops_config = [{
+            "op_type": "conv2d",
+            "op_inputs": {
+                "Input": ["input_data"],
+                "Filter": ["input_weight"]
+            },
+            "op_outputs": {
+                "Output": ["conv_output"]
+            },
+            "op_attrs": {
+                "data_format": data_format,
+                "dilations": dilations,
+                "padding_algorithm": padding_algorithm,
+                "groups": groups,
+                "paddings": paddings,
+                "strides": strides
+            }
+        }, {
+            "op_type": "hard_swish",
+            "op_inputs": {
+                "X": ["conv_output"]
+            },
+            "op_outputs": {
+                "Out": ["swish_output"]
+            },
+            "op_attrs": {
+                "threshold": threshold,
+                "scale": scale,
+                "offset": offset
+            },
+        }]
+
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={
+                "input_weight": TensorConfig(data_gen=partial(generate_weight))
+            },
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input)),
+            },
+            outputs=["swish_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ["conv2d"], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False, passes=["conv_hard_swish_mkldnn_fuse_pass"])
+
+
+if __name__ == "__main__":
+    unittest.main()