# Copyright (c) 2020 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 unittest import paddle import paddle.distributed.fleet as fleet import paddle.distributed.fleet.base.role_maker as role_maker import os import paddle.fluid as fluid import numpy as np class TestFleetBase(unittest.TestCase): def setUp(self): os.environ["POD_IP"] = "127.0.0.1" os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001" os.environ["PADDLE_TRAINERS_NUM"] = "2" os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \ "127.0.0.1:36001,127.0.0.2:36001" def test_init(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) def test_is_first_worker(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_first_worker(): print("test fleet first worker done.") def test_worker_index(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) print(fleet.worker_index()) def test_worker_num(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) print(fleet.worker_num()) def test_is_worker(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_worker(): print("test fleet is worker") def test_worker_endpoints(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) print(fleet.worker_endpoints(to_string=True)) def test_server_num(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_server(): print("fleet server num: {}".format(fleet.server_num())) def test_server_index(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_server(): print("fleet server index: {}".format(fleet.server_index())) def test_server_endpoints(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_server(): print("fleet server index: {}".format( fleet.server_endpoints(to_string=True))) def test_is_server(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_server(): print("test fleet is server") def test_util(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) self.assertEqual(fleet.util, None) def test_barrier_worker(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_worker(): fleet.barrier_worker() def test_init_worker(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_worker(): fleet.init_worker() def test_run_server(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_worker(): fleet.run_worker() def test_stop_worker(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) if fleet.is_worker(): fleet.stop_worker() def test_distributed_optimizer(self): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) optimizer = paddle.optimizer.SGD(learning_rate=0.001) optimizer = fleet.distributed_optimizer(optimizer) def test_exception(self): import paddle.distributed.fleet as fleet self.assertRaises(Exception, fleet.init_worker) class TestFleetDygraph(unittest.TestCase): def setUp(self): os.environ[ "PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36213,127.0.0.1:36214" os.environ["PADDLE_CURRENT_ENDPOINTS"] = "127.0.0.1:36213" os.environ["PADDLE_TRAINERS_NUM"] = "2" os.environ["PADDLE_TRAINER_ID"] = "0" def test_dygraph_method(self): paddle.disable_static() value = np.arange(26).reshape(2, 13).astype("float32") a = fluid.dygraph.to_variable(value) layer = paddle.nn.Linear(13, 5) adam = paddle.optimizer.Adam( learning_rate=0.01, parameters=layer.parameters()) # remove init cause this UT cannot launch distributed task adam = fleet.distributed_optimizer(adam) dp_layer = fleet.distributed_model(layer) lr = 0.001 adam.set_lr(lr) cur_lr = adam.get_lr() assert (lr == cur_lr) state_dict = adam.state_dict() adam.set_state_dict(state_dict) class TestFleetBaseSingleRunCollective(unittest.TestCase): def setUp(self): os.environ.pop("PADDLE_TRAINER_ENDPOINTS") def gen_data(self): return { "x": np.random.random(size=(128, 32)).astype('float32'), "y": np.random.randint( 2, size=(128, 1)).astype('int64') } def test_single_run_collective_minimize(self): input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32') input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64') fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh') prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') cost = fluid.layers.cross_entropy(input=prediction, label=input_y) avg_cost = paddle.mean(x=cost) fleet.init(is_collective=True) optimizer = fluid.optimizer.SGD(learning_rate=0.001) optimizer = fleet.distributed_optimizer(optimizer) optimizer.minimize(avg_cost) place = fluid.CUDAPlace(0) if paddle.fluid.is_compiled_with_cuda( ) else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(paddle.static.default_startup_program()) for i in range(10): cost_val = exe.run(feed=self.gen_data(), fetch_list=[avg_cost.name]) print("cost of step[{}] = {}".format(i, cost_val)) class TestFleetBaseSingleRunPS(unittest.TestCase): def setUp(self): os.environ.pop("PADDLE_PSERVERS_IP_PORT_LIST") def gen_data(self): return { "x": np.random.random(size=(128, 32)).astype('float32'), "y": np.random.randint( 2, size=(128, 1)).astype('int64') } def test_single_run_ps_minimize(self): input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32') input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64') fc_1 = fluid.layers.fc(input=input_x, size=64, act='tanh') prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') cost = fluid.layers.cross_entropy(input=prediction, label=input_y) avg_cost = paddle.mean(x=cost) fleet.init() strategy = paddle.distributed.fleet.DistributedStrategy() optimizer = fluid.optimizer.SGD(learning_rate=0.01) optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy) optimizer.minimize(avg_cost) if fleet.is_server(): fleet.init_server() fleet.run_server() elif fleet.is_worker(): place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(paddle.static.default_startup_program()) step = 100 for i in range(step): cost_val = exe.run(program=fluid.default_main_program(), feed=self.gen_data(), fetch_list=[avg_cost.name]) print("worker_index: %d, step%d cost = %f" % (fleet.worker_index(), i, cost_val[0])) fleet.save_persistables(exe, "fleet_single_model/") print("save fleet models done.") if __name__ == "__main__": unittest.main()