# 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 from functools import reduce SEED = 1 DTYPE = "float32" paddle.dataset.mnist.fetch() # random seed must set before configuring the network. # fluid.default_startup_program().random_seed = SEED def cnn_model(data): conv_pool_1 = fluid.nets.simple_img_conv_pool( input=data, filter_size=5, num_filters=20, pool_size=2, pool_stride=2, act="relu") conv_pool_2 = fluid.nets.simple_img_conv_pool( input=conv_pool_1, filter_size=5, num_filters=50, pool_size=2, pool_stride=2, act="relu") # TODO(dzhwinter) : refine the initializer and random seed settting SIZE = 10 input_shape = conv_pool_2.shape param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE] scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5 predict = fluid.layers.fc( input=conv_pool_2, size=SIZE, act="softmax", param_attr=fluid.param_attr.ParamAttr( initializer=fluid.initializer.NormalInitializer( loc=0.0, scale=scale))) return predict def get_model(batch_size): # Input data images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE) label = fluid.layers.data(name='label', shape=[1], dtype='int64') # Train program predict = cnn_model(images) cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(x=cost) # Evaluator batch_size_tensor = fluid.layers.create_tensor(dtype='int64') batch_acc = fluid.layers.accuracy( input=predict, label=label, total=batch_size_tensor) inference_program = fluid.default_main_program().clone() # Optimization opt = fluid.optimizer.AdamOptimizer( learning_rate=0.001, beta1=0.9, beta2=0.999) # Reader train_reader = paddle.batch( paddle.dataset.mnist.train(), batch_size=batch_size) test_reader = paddle.batch( paddle.dataset.mnist.test(), batch_size=batch_size) opt.minimize(avg_cost) return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict 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(batch_size=20) 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.SIGTERM) 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, batch_acc, predict = get_model( batch_size=20) 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()) feed_var_list = [ var for var in trainer_prog.global_block().vars.values() if var.is_data ] feeder = fluid.DataFeeder(feed_var_list, place) for pass_id in range(10): for batch_id, data in enumerate(train_reader()): exe.run(trainer_prog, feed=feeder.feed(data)) if (batch_id + 1) % 10 == 0: acc_set = [] avg_loss_set = [] for test_data in test_reader(): acc_np, avg_loss_np = exe.run( program=test_program, feed=feeder.feed(test_data), fetch_list=[batch_acc, avg_cost]) acc_set.append(float(acc_np)) avg_loss_set.append(float(avg_loss_np)) # get test acc and loss acc_val = np.array(acc_set).mean() avg_loss_val = np.array(avg_loss_set).mean() if float(acc_val ) > 0.8: # Smaller value to increase CI speed return else: print( 'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'. format(pass_id, batch_id + 1, float(avg_loss_val), float(acc_val))) if math.isnan(float(avg_loss_val)): assert ("got Nan loss, training failed.") if __name__ == "__main__": unittest.main()