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paddlepaddle--paddle/test/legacy_test/test_dist_fleet_gloo.py
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2026-07-13 12:40:42 +08:00

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Python

# 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 os
import shutil
import subprocess
import tempfile
import time
import unittest
# import paddle.incubate.distributed.fleet.role_maker as role_maker
from test_dist_fleet_base import TestFleetBase
# from dist_simnet_bow import train_network
class TestDistGloo_2x2(TestFleetBase):
def _setup_config(self):
self._mode = "sync"
self._reader = "pyreader"
self._path = "./tmp4"
if os.path.exists(self._path):
shutil.rmtree(self._path)
# if not os.path.exists(self._path):
# os.mkdir(self._path)
def _start_pserver(self, cmd, required_envs):
# env.update(required_envs)
ps0_cmd = cmd
ps1_cmd = cmd
ps0_pipe = open(tempfile.gettempdir() + "/ps0_err.log", "wb+")
ps1_pipe = open(tempfile.gettempdir() + "/ps1_err.log", "wb+")
required_envs["POD_IP"] = "127.0.0.1"
required_envs["PADDLE_PSERVER_ID"] = "0"
required_envs["PADDLE_PORT"] = "36011"
ps0_proc = subprocess.Popen(
ps0_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=ps0_pipe,
env=required_envs,
)
print("PADDLE_PSERVER_ID=0:")
print(required_envs)
required_envs["PADDLE_PSERVER_ID"] = "1"
required_envs["PADDLE_PORT"] = "36012"
ps1_proc = subprocess.Popen(
ps1_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=ps1_pipe,
env=required_envs,
)
print("PADDLE_PSERVER_ID=1:")
print(required_envs)
return ps0_proc, ps1_proc, ps0_pipe, ps1_pipe
def _start_trainer(self, cmd, required_envs):
# env.update(required_envs)
tr0_cmd = cmd
tr1_cmd = cmd
tr0_pipe = open(tempfile.gettempdir() + "/tr0_err.log", "wb+")
tr1_pipe = open(tempfile.gettempdir() + "/tr1_err.log", "wb+")
required_envs["PADDLE_TRAINER_ID"] = "0"
tr0_proc = subprocess.Popen(
tr0_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=tr0_pipe,
env=required_envs,
)
print("PADDLE_TRAINER_ID=0:")
print(required_envs)
required_envs["PADDLE_TRAINER_ID"] = "1"
tr1_proc = subprocess.Popen(
tr1_cmd.strip().split(" "),
stdout=subprocess.PIPE,
stderr=tr1_pipe,
env=required_envs,
)
print("PADDLE_TRAINER_ID=1:")
print(required_envs)
return tr0_proc, tr1_proc, tr0_pipe, tr1_pipe
def _run_cluster(self, model, envs):
env = {'GRAD_CLIP': str(self._grad_clip_mode)}
python_path = self._python_interp
if os.getenv('WITH_COVERAGE', 'OFF') == 'ON':
envs['COVERAGE_FILE'] = os.getenv('COVERAGE_FILE', '')
python_path += " -m coverage run --branch -p"
env.update(envs)
tr_cmd = f"{python_path} {model}"
ps_cmd = f"{python_path} {model}"
# Run dist train to compare with local results
env["TRAINING_ROLE"] = "PSERVER"
ps0, ps1, ps0_pipe, ps1_pipe = self._start_pserver(ps_cmd, env)
print(ps_cmd)
env["TRAINING_ROLE"] = "TRAINER"
tr0, tr1, tr0_pipe, tr1_pipe = self._start_trainer(tr_cmd, env)
# Wait until trainer process terminate
while True:
stat0 = tr0.poll()
time.sleep(0.1)
if stat0 is not None:
break
while True:
stat1 = tr1.poll()
time.sleep(0.1)
if stat1 is not None:
break
tr0_out, tr0_err = tr0.communicate()
tr1_out, tr1_err = tr1.communicate()
tr0_ret = tr0.returncode
tr1_ret = tr0.returncode
self.assertEqual(tr0_ret, 0, "something wrong in tr0, please check")
self.assertEqual(tr1_ret, 0, "something wrong in tr1, please check")
# close trainer file
tr0_pipe.close()
tr1_pipe.close()
ps0_pipe.close()
ps1_pipe.close()
ps0.terminate()
ps1.terminate()
return 0, 0
def check_with_place(
self, model_file, delta=1e-3, check_error_log=False, need_envs={}
):
required_envs = {
"PATH": os.getenv("PATH", ""),
"PYTHONPATH": os.getenv("PYTHONPATH", ""),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
"http_proxy": "",
"CPU_NUM": "2",
# PSERVER
"PADDLE_PSERVERS_IP_PORT_LIST": "127.0.0.1:36011,127.0.0.1:36012",
# "PADDLE_PSERVER_PORT_ARRAY":"(36011 36012)",
"PADDLE_PSERVER_NUMS": "2",
"PADDLE_TRAINER_ID": "0",
# TRAINER
"PADDLE_TRAINER_ENDPOINTS": "127.0.0.1:36013,127.0.0.1:36014",
"PADDLE_TRAINERS_NUM": "2",
"PADDLE_PSERVER_ID": "0",
# GLOO FLAG
"PADDLE_WITH_GLOO": "1",
}
required_envs.update(need_envs)
if check_error_log:
required_envs["GLOG_v"] = "3"
required_envs["GLOG_logtostderr"] = "1"
tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
def test_dist_train(self):
print("path is not delete", os.path.exists("./tmp4"))
self.check_with_place(
"dist_fleet_debug_gloo.py", delta=1e-5, check_error_log=True
)
if __name__ == "__main__":
unittest.main()