wehub-resource-sync
488 lines
18 KiB
Python
488 lines
18 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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high level unit test for distribute fleet.
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"""
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import argparse
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import os
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import shutil
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import socket
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import subprocess
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import sys
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import tempfile
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import time
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import unittest
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from contextlib import closing
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import paddle
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from paddle.distributed import fleet
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from paddle.distributed.fleet.base import role_maker
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__all__ = ['FleetDistHeterRunnerBase', 'TestFleetHeterBase', 'runtime_main']
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RUN_STEP = 5
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LEARNING_RATE = 0.01
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DIST_UT_PORT = 0
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class FleetDistHeterRunnerBase:
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"""
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run_pserver,run_trainer : after init role, using transpiler split program
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net : implement by child class, the network of model
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do training : exe run program
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"""
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def build_role(self, args):
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environs = {}
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heter_trainer_endpoints = args.heter_trainer_endpoints.split(";")
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all_heter_trainer_endpoints = ",".join(heter_trainer_endpoints)
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if args.role.upper() == "PSERVER":
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environs["PADDLE_PSERVERS_IP_PORT_LIST"] = args.endpoints
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environs["PADDLE_TRAINER_ENDPOINTS"] = args.trainer_endpoints
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environs["PADDLE_ALL_HETER_TRAINER_IP_PORT_LIST"] = (
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all_heter_trainer_endpoints
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)
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environs["POD_IP"] = args.endpoints.split(",")[
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int(args.current_id)
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].split(":")[0]
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environs["PADDLE_PORT"] = args.endpoints.split(",")[
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int(args.current_id)
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].split(":")[1]
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environs["TRAINING_ROLE"] = args.role.upper()
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environs["PADDLE_TRAINERS_NUM"] = args.trainers
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elif args.role.upper() == "HETER_TRAINER":
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previous_endpoints = (
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args.trainer_endpoints
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if args.stage_id == 2
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else heter_trainer_endpoints[0]
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)
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next_endpoints = (
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heter_trainer_endpoints[1] if args.stage_id == 2 else ""
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)
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heter_device = args.heter_trainer_device.split(";")[
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args.stage_id - 2
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]
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environs["PADDLE_PSERVERS_IP_PORT_LIST"] = args.endpoints
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environs["PADDLE_TRAINER_ENDPOINTS"] = args.trainer_endpoints
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environs["PADDLE_NEXT_HETER_TRAINER_IP_PORT_LIST"] = next_endpoints
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environs["PADDLE_PREVIOUS_HETER_TRAINER_IP_PORT_LIST"] = (
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previous_endpoints
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)
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environs["PADDLE_ALL_HETER_TRAINER_IP_PORT_LIST"] = (
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all_heter_trainer_endpoints
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)
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environs["HETER_DEVICE_TYPE"] = heter_device
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environs["TRAINING_ROLE"] = args.role.upper()
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environs["POD_IP"] = all_heter_trainer_endpoints.split(",")[
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int(args.current_id)
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].split(":")[0]
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environs["PADDLE_PORT"] = all_heter_trainer_endpoints.split(",")[
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int(args.current_id)
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].split(":")[1]
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environs["PADDLE_TRAINERS_NUM"] = args.trainers
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environs["PADDLE_STAGE_TRAINERS_NUM"] = [2, 2, 2]
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environs["FLAGS_selected_gpus"] = 0
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environs["FLAGS_selected_xpus"] = 0
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environs["CUDA_VISIBLE_DEVICES"] = 0
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environs["XPU_VISIBLE_DEVICES"] = 0
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environs["STAGE_ID"] = args.stage_id
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environs["STAGE_NUM"] = 3
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elif args.role.upper() == "TRAINER":
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environs["PADDLE_PSERVERS_IP_PORT_LIST"] = args.endpoints
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environs["PADDLE_TRAINER_ENDPOINTS"] = args.trainer_endpoints
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environs["PADDLE_NEXT_HETER_TRAINER_IP_PORT_LIST"] = (
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heter_trainer_endpoints[0]
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)
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environs["PADDLE_PREVIOUS_HETER_TRAINER_IP_PORT_LIST"] = ""
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environs["PADDLE_ALL_HETER_TRAINER_IP_PORT_LIST"] = (
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all_heter_trainer_endpoints
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)
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environs["HETER_DEVICE_TYPE"] = "cpu"
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environs["TRAINING_ROLE"] = args.role.upper()
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environs["PADDLE_TRAINER_ID"] = args.current_id
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environs["POD_IP"] = args.trainer_endpoints.split(",")[
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int(args.current_id)
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].split(":")[0]
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environs["PADDLE_PORT"] = args.trainer_endpoints.split(",")[
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int(args.current_id)
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].split(":")[1]
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environs["PADDLE_TRAINERS_NUM"] = args.trainers
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environs["PADDLE_STAGE_TRAINERS_NUM"] = [2, 2, 2]
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environs["FLAGS_selected_gpus"] = 0
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environs["FLAGS_selected_xpus"] = 0
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environs["CUDA_VISIBLE_DEVICES"] = 0
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environs["XPU_VISIBLE_DEVICES"] = 0
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environs["STAGE_ID"] = 1
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environs["STAGE_NUM"] = 3
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for k, v in environs.items():
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print(k, v)
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os.environ[k] = str(v)
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self.role = role_maker.PaddleCloudRoleMaker()
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return self.role
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def build_strategy(self, args):
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self.strategy = paddle.distributed.fleet.DistributedStrategy()
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self.strategy.a_sync = True
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self.strategy.a_sync_configs = {
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"launch_barrier": True,
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"heter_worker_device_guard": 'gpu',
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}
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self.strategy.pipeline = True
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self.strategy.pipeline_configs = {
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"accumulate_steps": 1,
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"micro_batch_size": 2048,
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}
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return self.strategy
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def build_optimizer(self, avg_cost, strategy):
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optimizer = paddle.optimizer.SGD(LEARNING_RATE)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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optimizer.minimize(avg_cost)
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def run_pserver(self, args):
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fleet.init_server()
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fleet.run_server()
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def run_dataset_heter_trainer(self, args):
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out = self.do_dataset_heter_training(fleet)
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def run_dataset_trainer(self, args):
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out = self.do_dataset_training(fleet)
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def net(self, args, batch_size=4, lr=0.01):
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raise NotImplementedError(
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"get_model should be implemented by child classes."
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)
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def do_dataset_training(self, fleet):
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raise NotImplementedError(
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"do_dataset_training should be implemented by child classes."
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)
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def do_dataset_heter_training(self, fleet):
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raise NotImplementedError(
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"do_dataset_heter_training should be implemented by child classes."
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)
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class TestFleetHeterBase(unittest.TestCase):
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"""
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start_pserver,start_trainer : add start cmd to test
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run_cluster : using multi process to test distribute program
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"""
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def _setup_config(self):
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raise NotImplementedError("tests should have _setup_config implemented")
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def tearDown(self):
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t = time.time() - self.startTime
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print(f'{self.__class__.__name__}: {t:.3f}')
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def setUp(self):
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self.startTime = time.time()
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self._mode = "async"
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self._reader = "dataset"
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self._trainers = 2
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self._pservers = 2
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self._port_set = set()
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self._heter_device = "gpu;cpu"
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global DIST_UT_PORT
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if DIST_UT_PORT == 0 and os.getenv("PADDLE_DIST_UT_PORT"):
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DIST_UT_PORT = int(os.getenv("PADDLE_DIST_UT_PORT"))
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if DIST_UT_PORT:
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print("set begin_port:", DIST_UT_PORT)
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self._ps_endpoints = (
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f"127.0.0.1:{DIST_UT_PORT},127.0.0.1:{DIST_UT_PORT + 1}"
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)
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self._tr_endpoints = (
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f"127.0.0.1:{DIST_UT_PORT + 2},127.0.0.1:{DIST_UT_PORT + 3}"
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)
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self._heter_endpoints = (
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f"127.0.0.1:{DIST_UT_PORT + 4},127.0.0.1:{DIST_UT_PORT + 5}"
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)
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self._heter_endpoints_2 = (
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f"127.0.0.1:{DIST_UT_PORT + 6},127.0.0.1:{DIST_UT_PORT + 7}"
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)
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DIST_UT_PORT += 8
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else:
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self._ps_endpoints = f"127.0.0.1:{self._find_free_port()},127.0.0.1:{self._find_free_port()}"
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self._tr_endpoints = f"127.0.0.1:{self._find_free_port()},127.0.0.1:{self._find_free_port()}"
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self._heter_endpoints = f"127.0.0.1:{self._find_free_port()},127.0.0.1:{self._find_free_port()}"
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self._heter_endpoints_2 = f"127.0.0.1:{self._find_free_port()},127.0.0.1:{self._find_free_port()}"
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self._python_interp = sys.executable
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self._geo_sgd_need_push_nums = 5
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self._grad_clip_mode = 0
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self._setup_config()
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def _find_free_port(self):
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def __free_port():
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with closing(
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socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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) as s:
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s.bind(('', 0))
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return s.getsockname()[1]
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while True:
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port = __free_port()
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if port not in self._port_set:
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self._port_set.add(port)
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return port
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def _start_pserver(self, cmd, required_envs):
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ps0_cmd, ps1_cmd = cmd.format(0), cmd.format(1)
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ps0_pipe = open(tempfile.gettempdir() + "/ps0_err.log", "wb+")
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ps1_pipe = open(tempfile.gettempdir() + "/ps1_err.log", "wb+")
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ps0_proc = subprocess.Popen(
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ps0_cmd.strip().split(" "),
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stdout=subprocess.PIPE,
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stderr=ps0_pipe,
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env=required_envs,
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)
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ps1_proc = subprocess.Popen(
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ps1_cmd.strip().split(" "),
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stdout=subprocess.PIPE,
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stderr=ps1_pipe,
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env=required_envs,
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)
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return ps0_proc, ps1_proc, ps0_pipe, ps1_pipe
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def _start_trainer(self, cmd, required_envs):
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tr0_cmd, tr1_cmd = cmd.format(0), cmd.format(1)
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tr0_pipe = open(tempfile.gettempdir() + "/tr0_err.log", "wb+")
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tr1_pipe = open(tempfile.gettempdir() + "/tr1_err.log", "wb+")
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tr0_out = open(tempfile.gettempdir() + "/tr0_out.log", "wb+")
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tr1_out = open(tempfile.gettempdir() + "/tr1_out.log", "wb+")
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tr0_proc = subprocess.Popen(
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tr0_cmd.strip().split(" "),
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stdout=tr0_out,
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stderr=tr0_pipe,
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env=required_envs,
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)
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tr1_proc = subprocess.Popen(
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tr1_cmd.strip().split(" "),
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stdout=tr1_out,
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stderr=tr1_pipe,
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env=required_envs,
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)
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return tr0_proc, tr1_proc, tr0_pipe, tr1_pipe
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def _start_heter_trainer(self, cmd, required_envs):
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heter0_cmd, heter1_cmd, heter2_cmd, heter3_cmd = (
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cmd.format(0, 2),
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cmd.format(1, 2),
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cmd.format(2, 3),
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cmd.format(3, 3),
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)
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heter0_pipe = open(tempfile.gettempdir() + "/heter0_err.log", "wb+")
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heter1_pipe = open(tempfile.gettempdir() + "/heter1_err.log", "wb+")
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heter2_pipe = open(tempfile.gettempdir() + "/heter2_err.log", "wb+")
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heter3_pipe = open(tempfile.gettempdir() + "/heter3_err.log", "wb+")
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heter0_out = open(tempfile.gettempdir() + "/heter0_out.log", "wb+")
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heter1_out = open(tempfile.gettempdir() + "/heter1_out.log", "wb+")
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heter2_out = open(tempfile.gettempdir() + "/heter2_out.log", "wb+")
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heter3_out = open(tempfile.gettempdir() + "/heter3_out.log", "wb+")
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heter0_proc = subprocess.Popen(
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heter0_cmd.strip().split(" "),
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stdout=heter0_out,
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stderr=heter0_pipe,
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env=required_envs,
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)
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heter1_proc = subprocess.Popen(
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heter1_cmd.strip().split(" "),
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stdout=heter1_out,
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stderr=heter1_pipe,
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env=required_envs,
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)
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heter2_proc = subprocess.Popen(
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heter2_cmd.strip().split(" "),
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stdout=heter2_out,
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stderr=heter2_pipe,
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env=required_envs,
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)
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heter3_proc = subprocess.Popen(
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heter3_cmd.strip().split(" "),
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stdout=heter3_out,
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stderr=heter3_pipe,
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env=required_envs,
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)
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return (
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heter0_proc,
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heter1_proc,
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heter2_proc,
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heter3_proc,
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heter0_pipe,
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heter1_pipe,
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heter2_pipe,
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heter3_pipe,
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)
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def _run_cluster(self, model, envs):
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env = {
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'GRAD_CLIP': str(self._grad_clip_mode),
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'FLAGS_eager_delete_tensor_gb': str(-1),
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}
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python_path = self._python_interp
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gloo_path = tempfile.mkdtemp()
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if os.getenv('WITH_COVERAGE', 'OFF') == 'ON':
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envs['COVERAGE_FILE'] = os.getenv('COVERAGE_FILE', '')
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python_path += " -m coverage run --branch -p"
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env.update(envs)
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self._all_heter_endpoints = ";".join(
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(self._heter_endpoints, self._heter_endpoints_2)
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)
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tr_cmd = f"{python_path} {model} --role trainer --endpoints {self._ps_endpoints} --trainer_endpoints {self._tr_endpoints} --current_id {{}} --trainers {self._trainers} --mode {self._mode} --geo_sgd_need_push_nums {self._geo_sgd_need_push_nums} --reader {self._reader} --gloo_path {gloo_path} --heter_trainer_endpoints {self._all_heter_endpoints} --heter_trainer_device {self._heter_device}"
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ps_cmd = f"{python_path} {model} --role pserver --endpoints {self._ps_endpoints} --trainer_endpoints {self._tr_endpoints} --current_id {{}} --trainers {self._trainers} --mode {self._mode} --geo_sgd_need_push_nums {self._geo_sgd_need_push_nums} --reader {self._reader} --gloo_path {gloo_path} --heter_trainer_endpoints {self._all_heter_endpoints} --heter_trainer_device {self._heter_device}"
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heter_cmd = f"{python_path} {model} --role heter_trainer --endpoints {self._ps_endpoints} --trainer_endpoints {self._tr_endpoints} --current_id {{}} --stage_id {{}} --trainers {self._trainers} --mode {self._mode} --geo_sgd_need_push_nums {self._geo_sgd_need_push_nums} --reader {self._reader} --gloo_path {gloo_path} --heter_trainer_endpoints {self._all_heter_endpoints} --heter_trainer_device {self._heter_device}"
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# Run dist train to compare with local results
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ps0, ps1, ps0_pipe, ps1_pipe = self._start_pserver(ps_cmd, env)
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tr0, tr1, tr0_pipe, tr1_pipe = self._start_trainer(tr_cmd, env)
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(
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heter0,
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heter1,
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heter2,
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heter3,
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heter0_pipe,
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heter1_pipe,
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heter2_pipe,
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heter3_pipe,
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) = self._start_heter_trainer(heter_cmd, env)
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# Wait until trainer process terminate
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while True:
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stat0 = tr0.poll()
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time.sleep(0.1)
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if stat0 is not None:
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break
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while True:
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stat1 = tr1.poll()
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time.sleep(0.1)
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if stat1 is not None:
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break
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tr0_out, tr0_err = tr0.communicate()
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tr1_out, tr1_err = tr1.communicate()
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print("tr end communicate")
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tr0_ret = tr0.returncode
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tr1_ret = tr1.returncode
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# close trainer file
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tr0_pipe.close()
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tr1_pipe.close()
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ps0_pipe.close()
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ps1_pipe.close()
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heter0_pipe.close()
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heter1_pipe.close()
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heter2_pipe.close()
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heter3_pipe.close()
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ps0.terminate()
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ps1.terminate()
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heter0.terminate()
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heter1.terminate()
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heter2.terminate()
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heter3.terminate()
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self.assertEqual(tr0_ret, 0, "something wrong in tr0, please check")
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self.assertEqual(tr1_ret, 0, "something wrong in tr1, please check")
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shutil.rmtree(gloo_path)
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return 0, 0
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def check_with_place(
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self, model_file, delta=1e-3, check_error_log=False, need_envs={}
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):
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": "",
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}
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required_envs.update(need_envs)
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if check_error_log:
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required_envs["GLOG_v"] = "3"
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required_envs["GLOG_logtostderr"] = "1"
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tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
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def runtime_main(test_class):
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parser = argparse.ArgumentParser(description='Run Fleet test.')
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parser.add_argument(
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'--role',
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type=str,
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required=True,
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choices=['pserver', 'trainer', 'heter_trainer'],
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)
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parser.add_argument('--endpoints', type=str, required=False, default="")
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parser.add_argument(
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'--trainer_endpoints', type=str, required=False, default=""
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)
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parser.add_argument(
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'--heter_trainer_endpoints', type=str, required=False, default=""
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)
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parser.add_argument(
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'--heter_trainer_device', type=str, required=False, default="gpu"
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)
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parser.add_argument('--gloo_path', type=str, required=False, default="")
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parser.add_argument('--current_id', type=int, required=False, default=0)
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parser.add_argument('--trainers', type=int, required=False, default=1)
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parser.add_argument('--stage_id', type=int, required=False, default=1)
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parser.add_argument('--mode', type=str, required=False, default='async')
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parser.add_argument(
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'--geo_sgd_need_push_nums', type=int, required=False, default=2
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)
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parser.add_argument('--reader', type=str, required=False, default='dataset')
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args = parser.parse_args()
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|
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model = test_class()
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role = model.build_role(args)
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fleet.init(role)
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strategy = model.build_strategy(args)
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avg_cost = model.net(args)
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model.build_optimizer(avg_cost, strategy)
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|
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if args.role == "pserver":
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model.run_pserver(args)
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elif args.role == "heter_trainer":
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model.run_dataset_heter_trainer(args)
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fleet.stop_worker()
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else:
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model.run_dataset_trainer(args)
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|
fleet.stop_worker()
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