make transpiler test reliable (#11848)
* make transpiler test reliable * add more * follow commentsanalysis/code-clean
Wu Yi
7 years ago
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# Copyright (c) 2018 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 numpy as np
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import paddle.fluid as fluid
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from paddle.fluid.transpiler.distribute_transpiler import delete_ops
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from transpiler_test import TranspilerTest
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class TestSimpleDistTranspiler(TranspilerTest):
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def setUp(self):
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self.current_pserver_ep = "127.0.0.1:6175"
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def test_simple_transpiler(self):
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np.random.seed(1)
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trainer = self.get_trainer()
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pserver, startup = self.get_pserver(self.current_pserver_ep)
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self.assertEqual([op.type for op in trainer.global_block().ops],
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self.get_expect_trainer_ops())
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self.assertEqual(len(pserver.blocks), 2)
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# block0: listen_and_serv
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self.assertEqual([op.type for op in pserver.blocks[0].ops],
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["listen_and_serv"])
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# block1: optimize pass
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self.assertEqual([op.type for op in pserver.blocks[1].ops],
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["sum", "scale", "sgd"])
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# confirm startup program
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self.assertEqual([op.type for op in startup.global_block().ops],
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["fill_constant", "uniform_random", "uniform_random"])
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# the variable #fc_w will NOT be splited
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fc_w_var = startup.global_block().var("fc_w@GRAD")
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self.assertEqual(fc_w_var.shape, (1000, 1000))
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fc_w_var = startup.global_block().var("fc_w@GRAD.trainer_0")
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self.assertEqual(fc_w_var.shape, (1000, 1000))
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def get_expect_trainer_ops(self):
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trainer = fluid.Program()
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with fluid.program_guard(trainer):
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optimize_ops, params_grads = self.net_conf()
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delete_ops(trainer.global_block(), optimize_ops)
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ops = [op.type for op in trainer.global_block().ops] + [
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"send", "send_barrier", "recv", "recv", "fetch_barrier"
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]
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ops.insert(ops.index("elementwise_add_grad") + 1, "send")
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return ops
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def _transpiler_instance(self):
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main = self.get_main_program()
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t = fluid.DistributeTranspiler()
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t.transpile(
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self.trainer_id,
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program=main,
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pservers=self.pserver_eps,
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trainers=self.trainers,
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slice_var_up=False)
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return t
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if __name__ == "__main__":
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unittest.main()
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# Copyright (c) 2018 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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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import paddle.fluid.layers as layers
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class TranspilerTest(unittest.TestCase):
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@classmethod
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def setUpClass(self):
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self.trainer_id = 0
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self.trainers = 2
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self.pservers = 2
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self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
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def net_conf(self):
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x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
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y_predict = fluid.layers.fc(input=x,
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size=1000,
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act=None,
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param_attr=fluid.ParamAttr(name='fc_w'))
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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cost = fluid.layers.square_error_cost(input=y_predict, label=y)
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avg_cost = fluid.layers.mean(cost)
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
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optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
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return optimize_ops, params_grads
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def get_main_program(self):
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main = fluid.Program()
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with fluid.program_guard(main):
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self.net_conf()
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return main
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def get_trainer(self):
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return self._transpiler_instance().get_trainer_program()
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def get_pserver(self, ep):
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t = self._transpiler_instance()
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pserver = t.get_pserver_program(ep)
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startup = t.get_startup_program(ep, pserver)
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return pserver, startup
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def _transpiler_instance(self):
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main = self.get_main_program()
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t = fluid.DistributeTranspiler()
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t.transpile(
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self.trainer_id,
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program=main,
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pservers=self.pserver_eps,
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trainers=self.trainers)
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return t
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