# 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. from __future__ import print_function import numpy as np import argparse import time import math import random import shutil import tempfile 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 from test_dist_fleet_base import runtime_main, FleetDistRunnerBase paddle.enable_static() DTYPE = "int64" DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/simnet.train.1000' DATA_MD5 = '24e49366eb0611c552667989de2f57d5' # For Net base_lr = 0.2 emb_lr = base_lr * 3 dict_dim = 1500 emb_dim = 128 hid_dim = 128 margin = 0.1 sample_rate = 1 # Fix seed for test fluid.default_startup_program().random_seed = 1 fluid.default_main_program().random_seed = 1 def fake_simnet_reader(): def reader(): for _ in range(1000): q = np.random.random_integers(0, 1500 - 1, size=1).tolist() label = np.random.random_integers(0, 1, size=1).tolist() pt = np.random.random_integers(0, 1500 - 1, size=1).tolist() nt = np.random.random_integers(0, 1500 - 1, size=1).tolist() yield [q, label, pt, nt] return reader def get_acc(cos_q_nt, cos_q_pt, batch_size): cond = fluid.layers.less_than(cos_q_nt, cos_q_pt) cond = fluid.layers.cast(cond, dtype='float64') cond_3 = fluid.layers.reduce_sum(cond) acc = fluid.layers.elementwise_div( cond_3, fluid.layers.fill_constant( shape=[1], value=batch_size * 1.0, dtype='float64'), name="simnet_acc") return acc def get_loss(cos_q_pt, cos_q_nt): loss_op1 = fluid.layers.elementwise_sub( fluid.layers.fill_constant_batch_size_like( input=cos_q_pt, shape=[-1, 1], value=margin, dtype='float32'), cos_q_pt) loss_op2 = fluid.layers.elementwise_add(loss_op1, cos_q_nt) loss_op3 = fluid.layers.elementwise_max( fluid.layers.fill_constant_batch_size_like( input=loss_op2, shape=[-1, 1], value=0.0, dtype='float32'), loss_op2) avg_cost = fluid.layers.mean(loss_op3) return avg_cost def train_network(batch_size, is_distributed=False, is_sparse=False, is_self_contained_lr=False, is_pyreader=False): # query q = fluid.layers.data( name="query_ids", shape=[1], dtype="int64", lod_level=1) # label data label = fluid.layers.data(name="label", shape=[1], dtype="int64") # pt pt = fluid.layers.data( name="pos_title_ids", shape=[1], dtype="int64", lod_level=1) # nt nt = fluid.layers.data( name="neg_title_ids", shape=[1], dtype="int64", lod_level=1) datas = [q, label, pt, nt] reader = None if is_pyreader: reader = fluid.io.PyReader( feed_list=datas, capacity=64, iterable=False, use_double_buffer=False) # embedding q_emb = fluid.embedding( input=q, is_distributed=is_distributed, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__"), is_sparse=is_sparse) q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim]) # vsum q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum') q_ss = fluid.layers.softsign(q_sum) # fc layer after conv q_fc = fluid.layers.fc( input=q_ss, size=hid_dim, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__q_fc__", learning_rate=base_lr), ) # embedding pt_emb = fluid.embedding( input=pt, is_distributed=is_distributed, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__", learning_rate=emb_lr), is_sparse=is_sparse) pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim]) # vsum pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum') pt_ss = fluid.layers.softsign(pt_sum) # fc layer pt_fc = fluid.layers.fc( input=pt_ss, size=hid_dim, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__fc__"), bias_attr=fluid.ParamAttr(name="__fc_b__")) # embedding nt_emb = fluid.embedding( input=nt, is_distributed=is_distributed, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__"), is_sparse=is_sparse) nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim]) # vsum nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum') nt_ss = fluid.layers.softsign(nt_sum) # fc layer nt_fc = fluid.layers.fc( input=nt_ss, size=hid_dim, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__fc__"), bias_attr=fluid.ParamAttr(name="__fc_b__")) cos_q_pt = fluid.layers.cos_sim(q_fc, pt_fc) cos_q_nt = fluid.layers.cos_sim(q_fc, nt_fc) # loss avg_cost = get_loss(cos_q_pt, cos_q_nt) # acc acc = get_acc(cos_q_nt, cos_q_pt, batch_size) return avg_cost, acc, cos_q_pt, reader class TestDistSimnetBow2x2(FleetDistRunnerBase): """ For test SimnetBow model, use Fleet api """ def net(self, args, batch_size=4, lr=0.01): avg_cost, _, predict, self.reader = \ train_network(batch_size=batch_size, is_distributed=False, is_sparse=True, is_self_contained_lr=False, is_pyreader=(args.reader == "pyreader")) self.avg_cost = avg_cost self.predict = predict return avg_cost def check_model_right(self, dirname): model_filename = os.path.join(dirname, "__model__") with open(model_filename, "rb") as f: program_desc_str = f.read() program = fluid.Program.parse_from_string(program_desc_str) with open(os.path.join(dirname, "__model__.proto"), "w") as wn: wn.write(str(program)) def do_pyreader_training(self, fleet): """ do training using dataset, using fetch handler to catch variable Args: fleet(Fleet api): the fleet object of Parameter Server, define distribute training role """ exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) fleet.init_worker() batch_size = 4 # reader train_reader = paddle.batch(fake_simnet_reader(), batch_size=batch_size) self.reader.decorate_sample_list_generator(train_reader) for epoch_id in range(1): self.reader.start() try: pass_start = time.time() while True: loss_val = exe.run(program=fluid.default_main_program(), fetch_list=[self.avg_cost.name]) loss_val = np.mean(loss_val) message = "TRAIN ---> pass: {} loss: {}\n".format(epoch_id, loss_val) fleet.util.print_on_rank(message, 0) pass_time = time.time() - pass_start except fluid.core.EOFException: self.reader.reset() def do_dataset_training(self, fleet): pass if __name__ == "__main__": runtime_main(TestDistSimnetBow2x2)