# Copyright (c) 2018 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. import numpy as np import argparse import time import math import paddle import paddle.fluid as fluid import paddle.fluid.profiler as profiler from paddle.fluid import core import unittest from multiprocessing import Process import os import signal IS_SPARSE = True EMBED_SIZE = 32 HIDDEN_SIZE = 256 N = 5 BATCH_SIZE = 32 ExecutionStrategy = core.ParallelExecutor.ExecutionStrategy def get_model(): def __network__(words): embed_first = fluid.layers.embedding( input=words[0], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=IS_SPARSE, param_attr='shared_w') embed_second = fluid.layers.embedding( input=words[1], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=IS_SPARSE, param_attr='shared_w') embed_third = fluid.layers.embedding( input=words[2], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=IS_SPARSE, param_attr='shared_w') embed_forth = fluid.layers.embedding( input=words[3], size=[dict_size, EMBED_SIZE], dtype='float32', is_sparse=IS_SPARSE, param_attr='shared_w') concat_embed = fluid.layers.concat( input=[embed_first, embed_second, embed_third, embed_forth], axis=1) hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid') predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax') cost = fluid.layers.cross_entropy(input=predict_word, label=words[4]) avg_cost = fluid.layers.mean(cost) return avg_cost, predict_word word_dict = paddle.dataset.imikolov.build_dict() dict_size = len(word_dict) first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64') second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64') third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64') forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64') next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64') avg_cost, predict_word = __network__( [first_word, second_word, third_word, forth_word, next_word]) inference_program = paddle.fluid.default_main_program().clone() sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) sgd_optimizer.minimize(avg_cost) train_reader = paddle.batch( paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE) test_reader = paddle.batch( paddle.dataset.imikolov.test(word_dict, N), BATCH_SIZE) return inference_program, avg_cost, train_reader, test_reader, predict_word def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers): t = fluid.DistributeTranspiler() t.transpile( trainer_id=trainer_id, program=main_program, pservers=pserver_endpoints, trainers=trainers) return t def run_pserver(pserver_endpoints, trainers, current_endpoint): get_model() t = get_transpiler(0, fluid.default_main_program(), pserver_endpoints, trainers) pserver_prog = t.get_pserver_program(current_endpoint) startup_prog = t.get_startup_program(current_endpoint, pserver_prog) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) exe.run(pserver_prog) class TestDistMnist(unittest.TestCase): def setUp(self): self._trainers = 1 self._pservers = 1 self._ps_endpoints = "127.0.0.1:9123" def start_pserver(self, endpoint): p = Process( target=run_pserver, args=(self._ps_endpoints, self._trainers, endpoint)) p.start() return p.pid def _wait_ps_ready(self, pid): retry_times = 5 while True: assert retry_times >= 0, "wait ps ready failed" time.sleep(1) try: # the listen_and_serv_op would touch a file which contains the listen port # on the /tmp directory until it was ready to process all the RPC call. os.stat("/tmp/paddle.%d.port" % pid) return except os.error: retry_times -= 1 def stop_pserver(self, pid): os.kill(pid, signal.SIGKILL) def test_with_place(self): p = fluid.CUDAPlace(0) if core.is_compiled_with_cuda( ) else fluid.CPUPlace() pserver_pid = self.start_pserver(self._ps_endpoints) self._wait_ps_ready(pserver_pid) self.run_trainer(p, 0) self.stop_pserver(pserver_pid) def run_trainer(self, place, trainer_id): test_program, avg_cost, train_reader, test_reader, predict = get_model() t = get_transpiler(trainer_id, fluid.default_main_program(), self._ps_endpoints, self._trainers) trainer_prog = t.get_trainer_program() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) use_gpu = True if core.is_compiled_with_cuda() else False exec_strategy = ExecutionStrategy() exec_strategy.use_cuda = use_gpu train_exe = fluid.ParallelExecutor( use_cuda=use_gpu, main_program=trainer_prog, loss_name=avg_cost.name, exec_strategy=exec_strategy) feed_var_list = [ var for var in trainer_prog.global_block().vars.itervalues() if var.is_data ] feeder = fluid.DataFeeder(feed_var_list, place) for pass_id in xrange(10): for batch_id, data in enumerate(train_reader()): avg_loss_np = train_exe.run(feed=feeder.feed(data), fetch_list=[avg_cost.name]) loss = np.array(avg_loss_np).mean() if float(loss) < 5.0: return if math.isnan(loss): assert ("Got Nan loss, training failed") if __name__ == "__main__": unittest.main()