# Copyright (c) 2019 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 paddle.fluid as fluid import paddle.fluid.layers as layers import numpy as np import os import paddle.fluid.core as core import unittest from paddle.fluid.layers.nn import _pull_box_sparse from paddle.fluid.transpiler import collective class TestTranspile(unittest.TestCase): """ TestCases for BoxPS Preload """ def get_transpile(self, mode, trainers="127.0.0.1:6174"): config = fluid.DistributeTranspilerConfig() config.mode = 'collective' config.collective_mode = mode t = fluid.DistributeTranspiler(config=config) return t def test_transpile(self): main_program = fluid.Program() startup_program = fluid.Program() t = self.get_transpile("single_process_multi_thread") t.transpile( trainer_id=0, startup_program=startup_program, trainers="127.0.0.1:6174", program=main_program) t = self.get_transpile("grad_allreduce") try: t.transpile( trainer_id=0, startup_program=startup_program, trainers="127.0.0.1:6174", program=main_program) except ValueError as e: print(e) def test_single_trainers(self): transpiler = collective.GradAllReduce(0) try: transpiler.transpile( startup_program=fluid.Program(), main_program=fluid.Program(), rank=1, endpoints="127.0.0.1:6174", current_endpoint="127.0.0.1:6174", wait_port="6174") except ValueError as e: print(e) transpiler = collective.LocalSGD(0) try: transpiler.transpile( startup_program=fluid.Program(), main_program=fluid.Program(), rank=1, endpoints="127.0.0.1:6174", current_endpoint="127.0.0.1:6174", wait_port="6174") except ValueError as e: print(e) class TestBoxPSPreload(unittest.TestCase): """ TestCases for BoxPS Preload """ def test_boxps_cpu(self): self.run_boxps_preload(True) def test_boxps_gpu(self): self.run_boxps_preload(False) def run_boxps_preload(self, is_cpu=True): x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0) y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0) emb_x, emb_y = _pull_box_sparse([x, y], size=2) emb_xp = _pull_box_sparse(x, size=2) concat = layers.concat([emb_x, emb_y], axis=1) fc = layers.fc(input=concat, name="fc", size=1, num_flatten_dims=1, bias_attr=False) loss = layers.reduce_mean(fc) layers.Print(loss) place = fluid.CPUPlace() if is_cpu or not core.is_compiled_with_cuda( ) else fluid.CUDAPlace(0) exe = fluid.Executor(place) batch_size = 2 def binary_print(slot, fout): fout.write(str(len(slot)) + " ") for e in slot: fout.write(str(e) + " ") batch1 = np.ones( (batch_size, 2, 1)).astype("int64").reshape(batch_size, 2, 1) filelist = [] place_str = "cpu" if is_cpu else "gpu" for i in range(2): filelist.append("test_hdfs_" + place_str + "_" + str(i)) for f in filelist: with open(f, "w") as fout: for ins in batch1: for slot in ins: binary_print(slot, fout) fout.write("\n") def create_dataset(): dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset") dataset.set_date("20190930") dataset.set_use_var([x, y]) dataset.set_batch_size(2) dataset.set_thread(1) dataset.set_filelist(filelist) return dataset datasets = [] datasets.append(create_dataset()) datasets.append(create_dataset()) optimizer = fluid.optimizer.SGD(learning_rate=0.5) optimizer = fluid.optimizer.PipelineOptimizer( optimizer, cut_list=[], place_list=[place], concurrency_list=[1], queue_size=1, sync_steps=-1) optimizer.minimize(loss) exe.run(fluid.default_startup_program()) datasets[0].load_into_memory() datasets[0].begin_pass() datasets[1].preload_into_memory() exe.train_from_dataset( program=fluid.default_main_program(), dataset=datasets[0], print_period=1) datasets[0].end_pass(True) datasets[1].wait_preload_done() datasets[1].begin_pass() exe.train_from_dataset( program=fluid.default_main_program(), dataset=datasets[1], print_period=1, debug=True) datasets[1].end_pass(False) for f in filelist: os.remove(f) if __name__ == '__main__': unittest.main()