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224 lines
8.1 KiB
224 lines
8.1 KiB
# 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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from __future__ import print_function
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import numpy as np
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import unittest
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import time
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import argparse
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import os
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import six
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import sys
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import subprocess
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import traceback
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import functools
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import pickle
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from contextlib import closing
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from six import string_types
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.unique_name as nameGen
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from paddle.fluid import core
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class TestCollectiveAPIRunnerBase(object):
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def get_model(self, train_prog, startup_prog, rank):
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raise NotImplementedError(
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"get model should be implemented by child class.")
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def run_trainer(self, args):
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train_prog = fluid.Program()
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startup_prog = fluid.Program()
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endpoints = args["endpoints"].split(",")
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rank = args["trainerid"]
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current_endpoint = args["currentendpoint"]
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nranks = 2
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result = self.get_model(train_prog, startup_prog, rank)
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paddle.distributed.init_parallel_env()
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if args['backend'] == 'nccl':
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device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
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place = fluid.CUDAPlace(
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device_id) #if args.use_gpu else fluid.CPUPlace()
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else:
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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np.random.seed(os.getpid())
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indata = np.random.random((10, 1000))
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fetch_list = []
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for elem in result:
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fetch_list.append(elem.name)
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out = exe.run(train_prog,
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feed={'tindata': indata},
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fetch_list=fetch_list)
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if six.PY2:
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print(pickle.dumps(out))
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else:
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sys.stdout.buffer.write(pickle.dumps(out))
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def runtime_main(test_class, col_type):
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args = {}
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model = test_class()
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args["deviceid"] = os.getenv("FLAGS_selected_gpus")
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args["trainerid"] = int(os.getenv("PADDLE_TRAINER_ID"))
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args["trainernum"] = int(os.getenv("PADDLE_TRAINERS_NUM"))
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args["endpoints"] = os.getenv('PADDLE_TRAINER_ENDPOINTS')
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args["currentendpoint"] = os.getenv("PADDLE_CURRENT_ENDPOINT")
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args["col_type"] = col_type
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args["backend"] = os.getenv("BACKEND")
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args["path_id"] = int(os.getenv("PATH_ID"))
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model.run_trainer(args)
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import paddle.compat as cpt
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import socket
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from contextlib import closing
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class TestDistBase(unittest.TestCase):
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def setUp(self):
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self._port_set = set()
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self._trainers = 2
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self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
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self._find_free_port(), self._find_free_port())
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self._python_interp = sys.executable
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def _find_free_port(self):
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def __free_port():
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with closing(socket.socket(socket.AF_INET,
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socket.SOCK_STREAM)) 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 _run_cluster(self, model_file, envs):
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worker_endpoints = self._ps_endpoints.split(",")
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w0_ep, w1_ep = worker_endpoints
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#print("w0_ep:",w0_ep," w1_ep:",w1_ep)
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env0 = {
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"FLAGS_selected_gpus": "0",
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"PADDLE_TRAINER_ID": "0",
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"PADDLE_TRAINERS_NUM": "2",
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"PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints,
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"PADDLE_CURRENT_ENDPOINT": w0_ep
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}
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env1 = {
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"FLAGS_selected_gpus": "1",
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"PADDLE_TRAINER_ID": "1",
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"PADDLE_TRAINERS_NUM": "2",
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"PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints,
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"PADDLE_CURRENT_ENDPOINT": w1_ep
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}
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#update environment
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env0.update(envs)
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env1.update(envs)
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tr_cmd = "%s %s"
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tr0_cmd = tr_cmd % (self._python_interp, model_file)
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tr1_cmd = tr_cmd % (self._python_interp, model_file)
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tr0_pipe = open("/tmp/tr0_err_%d.log" % os.getpid(), "w")
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tr1_pipe = open("/tmp/tr1_err_%d.log" % os.getpid(), "w")
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#print(tr0_cmd)
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tr0_proc = subprocess.Popen(
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tr0_cmd.strip().split(),
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stdout=subprocess.PIPE,
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stderr=tr0_pipe,
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env=env0)
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tr1_proc = subprocess.Popen(
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tr0_cmd.strip().split(),
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stdout=subprocess.PIPE,
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stderr=tr1_pipe,
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env=env1)
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tr0_out, tr0_err = tr0_proc.communicate()
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tr1_out, tr1_err = tr1_proc.communicate()
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sys.stderr.write('trainer 0 stderr: %s\n' % tr0_err)
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sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err)
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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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with open("/tmp/tr0_err_%d.log" % os.getpid(), "r") as f:
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sys.stderr.write('trainer 0 stderr file: %s\n' % f.read())
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with open("/tmp/tr1_err_%d.log" % os.getpid(), "r") as f:
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sys.stderr.write('trainer 1 stderr file: %s\n' % f.read())
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return pickle.loads(tr0_out), pickle.loads(
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tr1_out), tr0_proc.pid, tr1_proc.pid
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def check_with_place(self,
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model_file,
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col_type,
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backend="nccl",
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path_id="0",
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check_error_log=False,
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need_envs={}):
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required_envs = {
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"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
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"FLAGS_eager_delete_tensor_gb": "0.0",
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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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"LD_PRELOAD": os.getenv("LD_PRELOAD", ""),
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"GLOG_v": "0",
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"NCCL_P2P_DISABLE": "1",
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"BACKEND": backend,
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"PATH_ID": path_id
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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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required_envs["GLOO_LOG_LEVEL"] = "TRACE"
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tr0_out, tr1_out, pid0, pid1 = self._run_cluster(model_file,
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required_envs)
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np.random.seed(pid0)
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input1 = np.random.random((10, 1000))
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np.random.seed(pid1)
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input2 = np.random.random((10, 1000))
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if col_type == "allgather":
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need_result = np.vstack((input1, input2))
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tr_out0 = np.vstack((tr0_out[0], tr0_out[1]))
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tr_out1 = np.vstack((tr1_out[0], tr1_out[1]))
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self.assertTrue(np.allclose(tr_out0, need_result))
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self.assertTrue(np.allclose(tr_out1, need_result))
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elif col_type == "broadcast":
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need_result = input2
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self.assertTrue(np.allclose(tr0_out, need_result))
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self.assertTrue(np.allclose(tr1_out, need_result))
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elif col_type == "reduce":
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need_result = input1 + input2
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self.assertTrue(np.allclose(tr0_out, need_result))
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elif col_type == "scatter":
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need_result = input2
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need_result1 = need_result[0:need_result.shape[0] // 2]
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need_result2 = need_result[need_result.shape[0] // 2:]
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self.assertTrue(np.allclose(tr0_out, need_result1))
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self.assertTrue(np.allclose(tr1_out, need_result2))
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elif col_type == "allreduce":
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need_result = input1 + input2
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self.assertTrue(
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np.allclose(
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tr0_out, need_result, rtol=1e-05, atol=1e-05))
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self.assertTrue(
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np.allclose(
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tr1_out, need_result, rtol=1e-05, atol=1e-05))
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else:
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pass
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