[UT] mish op, conv+mish, fc+mish fuse passes (#39340)

* mish unit tests

* code format

* remove unused imports

* code format

* remove hard-coded shape values

* remove timeouts

* remove timeouts v2

* restore timeouts

python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py

@@ -441,6 +441,7 @@ class Quant2Int8MkldnnPass(object):
         graph = self._apply_pass(graph, 'conv_relu6_mkldnn_fuse_pass')
         graph = self._apply_pass(graph, 'conv_swish_mkldnn_fuse_pass')
         graph = self._apply_pass(graph, 'conv_hard_swish_mkldnn_fuse_pass')
+        graph = self._apply_pass(graph, 'conv_mish_mkldnn_fuse_pass')
         graph = self._apply_pass(graph, 'conv_hard_sigmoid_mkldnn_fuse_pass')
         graph = self._apply_pass(graph, 'conv_gelu_mkldnn_fuse_pass')
         graph = self._apply_pass(graph, 'fc_fuse_pass',

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

@@ -123,6 +123,7 @@ if (WITH_MKLDNN)
   set_tests_properties(test_mkldnn_conv_elementwise_add_fuse_pass PROPERTIES TIMEOUT 120)
   set_tests_properties(test_mkldnn_depthwise_conv_pass PROPERTIES TIMEOUT 120)
   set_tests_properties(test_mkldnn_reshape_transpose_matmul_fuse_pass PROPERTIES TIMEOUT 100)
+  set_tests_properties(test_mkldnn_mish_op PROPERTIES TIMEOUT 300)
   set_tests_properties(test_mkldnn_prelu_op PROPERTIES TIMEOUT 300)
   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)
@@ -134,5 +135,7 @@ if (WITH_MKLDNN)
   set_tests_properties(test_mkldnn_matmul_v2_transpose_reshape_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)
+  set_tests_properties(test_mkldnn_conv_mish_fuse_pass PROPERTIES TIMEOUT 300)
+  set_tests_properties(test_mkldnn_fc_mish_fuse_pass PROPERTIES TIMEOUT 300)
 endif()
 endif()

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

+# 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.
+
+from auto_scan_test import PassAutoScanTest
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+from functools import partial
+import unittest
+import hypothesis.strategies as st
+
+
+class TestConvMishMkldnnFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [op.attrs for op in 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]]))
+        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": "mish",
+            "op_inputs": {
+                "X": ["conv_output"]
+            },
+            "op_outputs": {
+                "Out": ["mish_output"]
+            },
+            "op_attrs": {},
+        }]
+
+        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=["mish_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_mish_mkldnn_fuse_pass"])
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+# 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.
+
+from auto_scan_test import PassAutoScanTest
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+import unittest
+import hypothesis.strategies as st
+
+
+class TestFCMishMkldnnFusePass(PassAutoScanTest):
+    def sample_program_config(self, draw):
+        x_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=128), min_size=2, max_size=3))
+        in_num_col_dims = len(x_shape) - 1
+        w_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=128), min_size=2, max_size=2))
+        w_shape[0] = int(np.prod(x_shape[in_num_col_dims:]))
+        fc_bias_shape = [w_shape[1]]
+
+        ops_config = [{
+            "op_type": "fc",
+            "op_inputs": {
+                "Input": ["fc_x"],
+                "W": ["fc_w"],
+                "Bias": ["fc_bias"]
+            },
+            "op_outputs": {
+                "Out": ["fc_out"]
+            },
+            "op_attrs": {
+                "activation_type": "",
+                "padding_weights": False,
+                "in_num_col_dims": in_num_col_dims,
+                "use_mkldnn": True
+            }
+        }, {
+            "op_type": "mish",
+            "op_inputs": {
+                "X": ["fc_out"]
+            },
+            "op_outputs": {
+                "Out": ["mish_output"]
+            },
+            "op_attrs": {},
+        }]
+
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={
+                "fc_w": TensorConfig(shape=w_shape),
+                "fc_bias": TensorConfig(shape=fc_bias_shape),
+            },
+            inputs={"fc_x": TensorConfig(shape=x_shape), },
+            outputs=["mish_output"])
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(
+            use_mkldnn=True, passes=["fc_act_mkldnn_fuse_pass"])
+        yield config, ["fc"], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(quant=False, passes=["fc_act_mkldnn_fuse_pass"])
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+# 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.
+
+from auto_scan_test import MkldnnAutoScanTest
+from program_config import TensorConfig, ProgramConfig, OpConfig
+import numpy as np
+from functools import partial
+import unittest
+from hypothesis import given
+import hypothesis.strategies as st
+
+
+class TestMkldnnMishOp(MkldnnAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        # if mode is channel, and in_shape is 1 rank
+        if len(program_config.inputs['input_data'].
+               shape) == 1 and program_config.ops[0].attrs['mode'] == 'channel':
+            return False
+        return True
+
+    def sample_program_configs(self, *args, **kwargs):
+        def generate_input(*args, **kwargs):
+            return np.random.random(kwargs['in_shape']).astype(np.float32)
+
+        mish_op = OpConfig(
+            type="mish",
+            inputs={"X": ["input_data"]},
+            outputs={"Out": ["output_data"]},
+            attrs={
+                "mode": kwargs['mode'],
+                "data_format": kwargs['data_format']
+            })
+
+        program_config = ProgramConfig(
+            ops=[mish_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_mkldnn=True)
+        yield config, (1e-5, 1e-5)
+
+    @given(
+        mode=st.sampled_from(['all', 'channel', 'element']),
+        data_format=st.sampled_from(['NCHW', 'NHWC']),
+        in_shape=st.lists(
+            st.integers(
+                min_value=1, max_value=32), min_size=1, max_size=4))
+    def test(self, *args, **kwargs):
+        self.run_test(quant=False, *args, **kwargs)
+
+
+if __name__ == "__main__":
+    unittest.main()