Add pass compatible and unit test. (#27377)
Wilber
4 years ago
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GitHub
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02606d45ef
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from inference_pass_test import InferencePassTest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.core import AnalysisConfig
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from paddle.fluid.core import PassVersionChecker
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class FcFusePassTest(InferencePassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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data = fluid.data(
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name="data", shape=[-1, 128, 768], dtype="float32")
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data_y = fluid.data(name="y", shape=[-1, 128, 768], dtype="float32")
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fc_out1 = fluid.layers.fc(input=data,
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size=3072,
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num_flatten_dims=2,
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act="relu")
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fc_out2 = fluid.layers.fc(input=fc_out1,
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size=768,
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num_flatten_dims=2)
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self.feeds = {"data": np.random.random((4, 128, 768)).astype("float32")}
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self.fetch_list = [fc_out2]
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def test_check_output(self):
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use_gpu = [False]
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if core.is_compiled_with_cuda():
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use_gpu.append(True)
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for i in range(len(use_gpu)):
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self.check_output_with_option(use_gpu[i])
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self.assertTrue(PassVersionChecker.IsCompatible('fc_fuse_pass'))
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if __name__ == "__main__":
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unittest.main()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from inference_pass_test import InferencePassTest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.core import PassVersionChecker
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class FcGruFusePassTest(InferencePassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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dict_dim, emb_dim = 128, 64
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data = fluid.data(
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name='step_data', shape=[None], dtype='int64', lod_level=1)
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emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
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hidden_dim = 512
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x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
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hidden = fluid.layers.dynamic_gru(
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input=x,
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size=hidden_dim,
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bias_attr=True,
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origin_mode=False,
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is_reverse=True)
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batch = 16
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lod_tensor = fluid.LoDTensor()
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lod_tensor.set(np.random.randint(
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0, dict_dim, size=[batch]).astype("int64"),
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fluid.CPUPlace())
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lod_tensor.set_lod([[0, batch]])
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self.feeds = {"step_data": lod_tensor}
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self.fetch_list = [hidden]
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def test_check_output(self):
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use_gpu = False
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self.check_output_with_option(use_gpu)
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self.assertTrue(PassVersionChecker.IsCompatible('fc_gru_fuse_pass'))
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class MulGruFusePassTest(InferencePassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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dict_dim, emb_dim = 128, 64
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data = fluid.data(
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name='step_data', shape=[None], dtype='int64', lod_level=1)
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emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
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hidden_dim = 512
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x = fluid.layers.fc(input=emb, size=hidden_dim * 3, bias_attr=False)
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hidden = fluid.layers.dynamic_gru(
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input=x,
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size=hidden_dim,
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bias_attr=True,
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origin_mode=False,
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is_reverse=True)
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batch = 16
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lod_tensor = fluid.LoDTensor()
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lod_tensor.set(np.random.randint(
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0, dict_dim, size=[batch]).astype("int64"),
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fluid.CPUPlace())
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lod_tensor.set_lod([[0, batch]])
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self.feeds = {"step_data": lod_tensor}
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self.fetch_list = [hidden]
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def test_check_output(self):
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use_gpu = False
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self.check_output_with_option(use_gpu)
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self.assertTrue(PassVersionChecker.IsCompatible('mul_gru_fuse_pass'))
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if __name__ == "__main__":
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unittest.main()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from inference_pass_test import InferencePassTest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.core import PassVersionChecker
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class MulLstmFusePassTest(InferencePassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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dict_dim, emb_dim = 128, 64
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hidden_dim = 512
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data = fluid.data(
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name='data', shape=[1], dtype='int64', lod_level=1)
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emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
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x = fluid.layers.fc(input=emb, size=hidden_dim * 4, bias_attr=False)
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forward, cell = fluid.layers.dynamic_lstm(
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input=x, size=hidden_dim * 4)
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batch = 16
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lod_tensor = fluid.LoDTensor()
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lod_tensor.set(np.random.randint(
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0, dict_dim, size=[batch]).astype("int64"),
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fluid.CPUPlace())
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lod_tensor.set_lod([[0, batch]])
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self.feeds = {"data": lod_tensor}
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self.fetch_list = [forward, cell]
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def test_check_output(self):
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use_gpu = False
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self.check_output_with_option(use_gpu)
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self.assertTrue(PassVersionChecker.IsCompatible('mul_lstm_fuse_pass'))
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if __name__ == "__main__":
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unittest.main()
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from inference_pass_test import InferencePassTest
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.core import AnalysisConfig
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from paddle.fluid.core import PassVersionChecker
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class SquaredMatSubFusePassTest(InferencePassTest):
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def setUp(self):
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with fluid.program_guard(self.main_program, self.startup_program):
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data_a = fluid.data(name="data_a", shape=[128, 1], dtype="float32")
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data_b = fluid.data(name="data_b", shape=[256, 1], dtype="float32")
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fc_a = fluid.layers.fc(data_a, size=256)
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fc_b = fluid.layers.fc(data_b, size=64)
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data_a_square = paddle.square(fc_a)
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data_b_square = paddle.square(fc_b)
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matmul_ab = paddle.matmul(fc_a, fc_b)
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matmul_ab_square = paddle.square(matmul_ab)
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matmul_square_ab = paddle.matmul(data_a_square, data_b_square)
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scale = paddle.fill_constant(shape=[1], value=0.5, dtype='float32')
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sub_val = paddle.elementwise_sub(matmul_ab_square, matmul_square_ab)
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squared_mat_sub_out = fluid.layers.elementwise_mul(sub_val, scale)
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self.feeds = {
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"data_a": np.random.random((128, 1)).astype("float32"),
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"data_b": np.random.random((256, 1)).astype("float32")
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}
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self.fetch_list = [squared_mat_sub_out]
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def test_check_output(self):
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use_gpu = False
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self.check_output_with_option(use_gpu)
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self.assertTrue(
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PassVersionChecker.IsCompatible('squared_mat_sub_fuse_pass'))
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if __name__ == "__main__":
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unittest.main()
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