Fix clone() bug. (#12583)
gongweibao
7 years ago
committed by
GitHub
parent
7b03b18d16
commit
842fb021b3
@ -0,0 +1,196 @@
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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 argparse
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import time
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import math
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.profiler as profiler
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from paddle.fluid import core
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import unittest
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from multiprocessing import Process
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import os
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import signal
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import collections
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SEED = 1
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DTYPE = "float32"
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paddle.dataset.mnist.fetch()
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# random seed must set before configuring the network.
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# fluid.default_startup_program().random_seed = SEED
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def cnn_model(data):
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conv_pool_1 = fluid.nets.simple_img_conv_pool(
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input=data,
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filter_size=5,
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num_filters=20,
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pool_size=2,
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pool_stride=2,
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act="relu")
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conv_pool_2 = fluid.nets.simple_img_conv_pool(
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input=conv_pool_1,
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filter_size=5,
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num_filters=50,
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pool_size=2,
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pool_stride=2,
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act="relu")
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# TODO(dzhwinter) : refine the initializer and random seed settting
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SIZE = 10
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input_shape = conv_pool_2.shape
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param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
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scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
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predict = fluid.layers.fc(
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input=conv_pool_2,
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size=SIZE,
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act="softmax",
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=scale)))
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return predict
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def get_model(batch_size):
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# Input data
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images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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# Train program
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predict = cnn_model(images)
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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# Evaluator
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batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
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batch_acc = fluid.layers.accuracy(
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input=predict, label=label, total=batch_size_tensor)
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inference_program = fluid.default_main_program().clone()
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# Optimization
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opt = fluid.optimizer.AdamOptimizer(
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learning_rate=0.001, beta1=0.9, beta2=0.999)
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# Reader
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=batch_size)
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test_reader = paddle.batch(
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paddle.dataset.mnist.test(), batch_size=batch_size)
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opt.minimize(avg_cost)
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return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
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def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers):
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t = fluid.DistributeTranspiler()
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t.transpile(
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trainer_id=trainer_id,
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program=main_program,
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pservers=pserver_endpoints,
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trainers=trainers)
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return t
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def operator_equal(a, b):
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for k, v in a.__dict__.iteritems():
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if isinstance(v, fluid.framework.Program) or \
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isinstance(v, fluid.framework.Block):
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continue
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elif isinstance(v, core.OpDesc):
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if v.serialize_to_string() != b.__dict__[k].serialize_to_string():
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raise ValueError("In operator_equal not equal:{0}\n".format(k))
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elif isinstance(v, collections.OrderedDict):
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v0 = sorted(v.iteritems(), key=lambda x: x[0])
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v1 = sorted(b.__dict__[k].iteritems(), key=lambda x: x[0])
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if v0 != v1:
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raise ValueError("In operator_equal not equal:{0}\n".format(k))
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elif (v != b.__dict__[k]):
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raise ValueError("In operator_equal not equal:{0}\n".format(k))
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return True
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def block_equal(a, b):
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for k, v in a.__dict__.iteritems():
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if isinstance(v, core.ProgramDesc) or isinstance(
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v, fluid.framework.Program) or isinstance(v, core.BlockDesc):
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continue
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elif k == "ops":
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for i in range(0, len(a.ops)):
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if not operator_equal(a.ops[i], b.ops[i]):
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raise ValueError("In block_equal not equal:{0}\n".format(k))
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assert (len(a.ops) == len(b.ops))
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elif isinstance(v, collections.OrderedDict):
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v0 = sorted(v.iteritems(), key=lambda x: x[0])
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v1 = sorted(b.__dict__[k].iteritems(), key=lambda x: x[0])
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if v0 != v1:
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raise ValueError("In block_equal not equal:{0}\n".format(k))
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elif (v != b.__dict__[k]):
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raise ValueError("In block_equal not equal:{0}\n".format(k))
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return True
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def program_equal(a, b):
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for k, v in a.__dict__.iteritems():
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if isinstance(v, core.ProgramDesc):
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continue
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elif k == 'blocks':
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for i in range(0, len(a.blocks)):
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if not block_equal(a.blocks[i], b.blocks[i]):
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raise ValueError("In operator_equal not equal:{0}\n".format(
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k))
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return False
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assert (len(a.blocks) == len(b.blocks))
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elif (v != b.__dict__[k]):
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raise ValueError("In program_equal not equal:{0}\n".format(k))
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return True
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class TestDistMnist(unittest.TestCase):
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def test_desc_clone(self):
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get_model(batch_size=20)
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pserver_endpoints = "127.0.0.1:9123"
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trainers = 1
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current_endpoint = "127.0.0.1:9123"
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t = get_transpiler(0,
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fluid.default_main_program(), pserver_endpoints,
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trainers)
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pserver_prog = t.get_pserver_program(current_endpoint)
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startup_prog = t.get_startup_program(current_endpoint, pserver_prog)
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main = pserver_prog.clone()
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startup = startup_prog.clone()
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self.assertTrue(program_equal(main, pserver_prog))
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self.assertTrue(program_equal(startup, startup_prog))
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if __name__ == "__main__":
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unittest.main()
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