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355 lines
15 KiB
355 lines
15 KiB
# 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 paddle.fluid as fluid
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from paddle.fluid.transpiler.distribute_transpiler import delete_ops
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import traceback
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class TranspilerTest(unittest.TestCase):
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def setUp(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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# NOTE: we do not actually bind this port
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self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
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self.pserver1_ep = "127.0.0.1:6174"
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self.pserver2_ep = "127.0.0.1:6175"
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self.sync_mode = True
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self.transpiler = None
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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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bias_attr=fluid.ParamAttr(name='fc_b'))
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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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sgd_optimizer.minimize(avg_cost)
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return
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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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self.origin_prog = main.clone()
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return main
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def get_trainer(self, config=None):
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t = self._transpiler_instance(config)
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return t.get_trainer_program()
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def get_pserver(self, ep, config=None):
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t = self._transpiler_instance(config)
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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, config=None):
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if not self.transpiler:
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main = self.get_main_program()
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self.transpiler = fluid.DistributeTranspiler(config=config)
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self.transpiler.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 self.transpiler
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class TestBasicModel(TranspilerTest):
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def test_transpiler(self):
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pserver, startup = self.get_pserver(self.pserver1_ep)
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pserver2, startup2 = self.get_pserver(self.pserver2_ep)
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trainer = self.get_trainer()
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self.assertEqual([op.type for op in trainer.global_block().ops], [
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'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
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'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
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'elementwise_add_grad', 'send', 'mul_grad', 'split_byref', 'send',
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'send_barrier', 'recv', 'recv', 'fetch_barrier', 'concat'
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])
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self.assertEqual(len(pserver.blocks), 3)
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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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# block1~2: 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", "fill_constant", "uniform_random"])
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# the variable #fc_w will be split into two blocks
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fc_w_var = startup.global_block().var("fc_w.block1")
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self.assertEqual(fc_w_var.shape, (500, 1000))
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# all parameters should be optimized on pserver
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pserver_params = []
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for prog in [pserver, pserver2]:
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for blk in prog.blocks:
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for op in blk.ops:
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if "Param" in op.input_names:
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param_name = op.input("Param")[0]
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is_block_idx = param_name.find(".block")
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if is_block_idx != -1:
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origin_param_name = param_name[:is_block_idx]
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else:
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origin_param_name = param_name
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pserver_params.append(origin_param_name)
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trainer_params = []
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for op in self.origin_prog.global_block().ops:
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if "Param" in op.input_names:
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trainer_params.append(op.input("Param")[0])
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self.assertEqual(set(pserver_params), set(trainer_params))
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class TestBasicModelWithLargeBlockSize(TranspilerTest):
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def test_transpiler(self):
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config = fluid.DistributeTranspilerConfig()
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config.min_block_size = 1048576
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pserver, startup = self.get_pserver(self.pserver1_ep, config)
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pserver2, startup2 = self.get_pserver(self.pserver2_ep, config)
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trainer = self.get_trainer(config)
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self.assertEqual([op.type for op in trainer.global_block().ops], [
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'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
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'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
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'elementwise_add_grad', 'send', 'mul_grad', 'send', 'send_barrier',
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'recv', 'recv', 'fetch_barrier'
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])
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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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# block1~2: 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", "fill_constant", "fill_constant"])
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# the variable #fc_w will be split into two blocks
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fc_w_var = startup2.global_block().var("fc_w")
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self.assertEqual(fc_w_var.shape, (1000L, 1000L))
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# all parameters should be optimized on pserver
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pserver_params = []
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for prog in [pserver, pserver2]:
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for blk in prog.blocks:
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for op in blk.ops:
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if "Param" in op.input_names:
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param_name = op.input("Param")[0]
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is_block_idx = param_name.find(".block")
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if is_block_idx != -1:
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origin_param_name = param_name[:is_block_idx]
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else:
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origin_param_name = param_name
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pserver_params.append(origin_param_name)
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trainer_params = []
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for op in self.origin_prog.global_block().ops:
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if "Param" in op.input_names:
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trainer_params.append(op.input("Param")[0])
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self.assertEqual(set(pserver_params), set(trainer_params))
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class TestNoSliceVar(TranspilerTest):
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def setUp(self):
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super(TestNoSliceVar, self).setUp()
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def test_transpiler(self):
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config = fluid.DistributeTranspilerConfig()
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config.slice_var_up = False
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_, startup = self.get_pserver(self.pserver1_ep, config)
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_, startup2 = self.get_pserver(self.pserver2_ep, config)
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if startup.global_block().vars.has_key("fc_w"):
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fc_w_var = startup.global_block().vars["fc_w"]
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elif startup2.global_block().vars.has_key("fc_w"):
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fc_w_var = startup2.global_block().vars["fc_w"]
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self.assertEqual(fc_w_var.shape, (1000, 1000))
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class TestLRDecay(TranspilerTest):
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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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bias_attr=fluid.ParamAttr(name='fc_b'))
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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(
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learning_rate=fluid.layers.exponential_decay(
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learning_rate=1.0,
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decay_steps=2100,
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decay_rate=0.1,
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staircase=True))
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sgd_optimizer.minimize(avg_cost)
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return
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def test_transpiler(self):
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pserver, startup = self.get_pserver(self.pserver1_ep)
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trainer = self.get_trainer()
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self.assertEqual(len(pserver.blocks), 4)
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lr_decay_ops = [op.type for op in pserver.blocks[1].ops]
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self.assertEqual(lr_decay_ops, [
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"increment", "cast", "fill_constant", "elementwise_div", "floor",
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"fill_constant", "elementwise_pow", "fill_constant",
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"elementwise_mul"
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])
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class TestLRDecayConditional(TranspilerTest):
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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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bias_attr=fluid.ParamAttr(name='fc_b'))
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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(
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learning_rate=fluid.layers.piecewise_decay([10000, 20000],
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[1.0, 0.5, 1.0]))
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sgd_optimizer.minimize(avg_cost)
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return
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def test_transpiler(self):
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pserver, startup = self.get_pserver(self.pserver1_ep)
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trainer = self.get_trainer()
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serv_op = pserver.blocks[0].ops[0]
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sub_blocks = []
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optimize_blocks = []
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for b in serv_op.attrs["optimize_blocks"]:
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optimize_blocks.append(b.idx)
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for b in pserver.blocks:
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if b.idx not in optimize_blocks:
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sub_blocks.append(b.idx)
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self.assertEqual(len(pserver.blocks), 7)
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lr_decay_ops = [op.type for op in pserver.blocks[1].ops]
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self.assertEqual(lr_decay_ops, [
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"increment", "cast", "fill_constant", "fill_constant", "less_than",
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"logical_not", "conditional_block", "fill_constant",
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"fill_constant", "less_than", "logical_not", "logical_and",
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"logical_and", "conditional_block", "fill_constant",
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"conditional_block"
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])
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# test the condition blocks
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for b in sub_blocks:
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if b == 0:
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continue
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block = pserver.blocks[b]
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self.assertEqual([op.type for op in block.ops], ["assign"])
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class TestL2Decay(TranspilerTest):
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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(
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input=x,
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size=1000,
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act=None,
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param_attr=fluid.ParamAttr(
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name='fc_w',
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regularizer=fluid.regularizer.L2Decay(),
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gradient_clip=fluid.clip.GradientClipByValue(0.1)),
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bias_attr=fluid.ParamAttr(name='fc_b'))
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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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sgd_optimizer.minimize(avg_cost)
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return
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def test_transpiler(self):
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pserver, startup = self.get_pserver(self.pserver1_ep)
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trainer = self.get_trainer()
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self.assertEqual(len(pserver.blocks), 3)
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self.assertEqual([op.type for op in pserver.blocks[1].ops],
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["sum", "scale", "clip", "sgd"])
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self.assertEqual(
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[op.type for op in pserver.blocks[2].ops],
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["sum", "scale", "clip", "scale", "elementwise_add", "sgd"])
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# TODO(typhoonzero): test clipping and L2Decay ops are removed from trainer
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class TestL2DecayWithPiecewise(TranspilerTest):
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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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bias_attr=fluid.ParamAttr(name='fc_b'))
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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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base_lr = 1.0
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bd = [1, 10, 20, 30]
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lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
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sgd_optimizer = fluid.optimizer.Momentum(
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learning_rate=fluid.layers.piecewise_decay(
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boundaries=bd, values=lr),
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momentum=0.9,
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regularization=fluid.regularizer.L2Decay(1e-4))
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sgd_optimizer.minimize(avg_cost)
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return
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def test_transpiler(self):
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pserver, startup = self.get_pserver(self.pserver1_ep)
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trainer = self.get_trainer()
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self.assertEqual(len(pserver.blocks), 9)
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self.assertEqual([op.type for op in pserver.blocks[1].ops], [
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"increment", "cast", "fill_constant", "fill_constant", "less_than",
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"logical_not", "conditional_block", "fill_constant",
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"fill_constant", "less_than", "logical_not", "logical_and",
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"logical_and", "conditional_block", "fill_constant",
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"fill_constant", "less_than", "logical_not", "logical_and",
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"logical_and", "conditional_block", "fill_constant",
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"fill_constant", "less_than", "logical_not", "logical_and",
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"logical_and", "conditional_block", "fill_constant",
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"conditional_block"
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])
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self.assertEqual(
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[op.type for op in pserver.blocks[7].ops],
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["sum", "scale", "scale", "elementwise_add", "momentum"])
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self.assertEqual(
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[op.type for op in pserver.blocks[8].ops],
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["sum", "scale", "scale", "elementwise_add", "momentum"])
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if __name__ == "__main__":
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unittest.main()
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