# 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. from __future__ import print_function import numpy as np import unittest import time import argparse import os import six import sys import subprocess import traceback import functools import pickle from contextlib import closing from six import string_types import paddle import paddle.fluid as fluid import paddle.fluid.unique_name as nameGen from paddle.fluid import core class TestCollectiveAPIRunnerBase(object): def get_model(self, train_prog, startup_prog, rank): raise NotImplementedError( "get model should be implemented by child class.") def run_trainer(self, args): train_prog = fluid.Program() startup_prog = fluid.Program() endpoints = args["endpoints"].split(",") rank = args["trainerid"] current_endpoint = args["currentendpoint"] nranks = 2 result = self.get_model(train_prog, startup_prog, rank) paddle.distributed.init_parallel_env() if args['backend'] == 'nccl': device_id = int(os.getenv("FLAGS_selected_gpus", "0")) place = fluid.CUDAPlace( device_id) #if args.use_gpu else fluid.CPUPlace() else: place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) np.random.seed(os.getpid()) indata = np.random.random((10, 1000)).astype("float32") fetch_list = [] for elem in result: fetch_list.append(elem.name) out = exe.run(train_prog, feed={'tindata': indata}, fetch_list=fetch_list) if six.PY2: print(pickle.dumps(out)) else: sys.stdout.buffer.write(pickle.dumps(out)) def runtime_main(test_class, col_type): args = {} model = test_class() args["deviceid"] = os.getenv("FLAGS_selected_gpus") args["trainerid"] = int(os.getenv("PADDLE_TRAINER_ID")) args["trainernum"] = int(os.getenv("PADDLE_TRAINERS_NUM")) args["endpoints"] = os.getenv('PADDLE_TRAINER_ENDPOINTS') args["currentendpoint"] = os.getenv("PADDLE_CURRENT_ENDPOINT") args["col_type"] = col_type args["backend"] = os.getenv("BACKEND") args["path_id"] = int(os.getenv("PATH_ID")) model.run_trainer(args) import paddle.compat as cpt import socket from contextlib import closing class TestDistBase(unittest.TestCase): def setUp(self): self._port_set = set() self._trainers = 2 self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % ( self._find_free_port(), self._find_free_port()) self._python_interp = sys.executable def _find_free_port(self): def __free_port(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) return s.getsockname()[1] while True: port = __free_port() if port not in self._port_set: self._port_set.add(port) return port def _run_cluster(self, model_file, envs): worker_endpoints = self._ps_endpoints.split(",") w0_ep, w1_ep = worker_endpoints #print("w0_ep:",w0_ep," w1_ep:",w1_ep) env0 = { "FLAGS_selected_gpus": "0", "PADDLE_TRAINER_ID": "0", "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints, "PADDLE_CURRENT_ENDPOINT": w0_ep } env1 = { "FLAGS_selected_gpus": "1", "PADDLE_TRAINER_ID": "1", "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ENDPOINTS": self._ps_endpoints, "PADDLE_CURRENT_ENDPOINT": w1_ep } #update environment env0.update(envs) env1.update(envs) tr_cmd = "%s %s" tr0_cmd = tr_cmd % (self._python_interp, model_file) tr1_cmd = tr_cmd % (self._python_interp, model_file) tr0_pipe = open("/tmp/tr0_err_%d.log" % os.getpid(), "w") tr1_pipe = open("/tmp/tr1_err_%d.log" % os.getpid(), "w") #print(tr0_cmd) tr0_proc = subprocess.Popen( tr0_cmd.strip().split(), stdout=subprocess.PIPE, stderr=tr0_pipe, env=env0) tr1_proc = subprocess.Popen( tr0_cmd.strip().split(), stdout=subprocess.PIPE, stderr=tr1_pipe, env=env1) tr0_out, tr0_err = tr0_proc.communicate() tr1_out, tr1_err = tr1_proc.communicate() sys.stderr.write('trainer 0 stderr: %s\n' % tr0_err) sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err) # close trainer file tr0_pipe.close() tr1_pipe.close() with open("/tmp/tr0_err_%d.log" % os.getpid(), "r") as f: sys.stderr.write('trainer 0 stderr file: %s\n' % f.read()) with open("/tmp/tr1_err_%d.log" % os.getpid(), "r") as f: sys.stderr.write('trainer 1 stderr file: %s\n' % f.read()) return pickle.loads(tr0_out), pickle.loads( tr1_out), tr0_proc.pid, tr1_proc.pid def check_with_place(self, model_file, col_type, backend="nccl", path_id="0", check_error_log=False, need_envs={}): with_gloo = '0' if backend == "nccl" else '1' required_envs = { "FLAGS_fraction_of_gpu_memory_to_use": "0.15", "FLAGS_eager_delete_tensor_gb": "0.0", "PATH": os.getenv("PATH"), "PYTHONPATH": os.getenv("PYTHONPATH", ""), "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""), "LD_PRELOAD": os.getenv("LD_PRELOAD", ""), "GLOG_v": "0", "NCCL_P2P_DISABLE": "1", "PADDLE_WITH_GLOO": with_gloo, "BACKEND": backend, "PATH_ID": path_id } required_envs.update(need_envs) if check_error_log: required_envs["GLOG_v"] = "3" required_envs["GLOG_logtostderr"] = "1" required_envs["GLOO_LOG_LEVEL"] = "TRACE" tr0_out, tr1_out, pid0, pid1 = self._run_cluster(model_file, required_envs) np.random.seed(pid0) input1 = np.random.random((10, 1000)) np.random.seed(pid1) input2 = np.random.random((10, 1000)) if col_type == "allgather": need_result = np.vstack((input1, input2)) tr_out0 = np.vstack((tr0_out[0], tr0_out[1])) tr_out1 = np.vstack((tr1_out[0], tr1_out[1])) self.assertTrue(np.allclose(tr_out0, need_result)) self.assertTrue(np.allclose(tr_out1, need_result)) elif col_type == "broadcast": need_result = input2 self.assertTrue(np.allclose(tr0_out, need_result)) self.assertTrue(np.allclose(tr1_out, need_result)) elif col_type == "reduce": need_result = input1 + input2 self.assertTrue(np.allclose(tr0_out, need_result)) elif col_type == "scatter": need_result = input2 need_result1 = need_result[0:need_result.shape[0] // 2] need_result2 = need_result[need_result.shape[0] // 2:] self.assertTrue(np.allclose(tr0_out, need_result1)) self.assertTrue(np.allclose(tr1_out, need_result2)) elif col_type == "allreduce": need_result = input1 + input2 self.assertTrue( np.allclose( tr0_out, need_result, rtol=1e-05, atol=1e-05)) self.assertTrue( np.allclose( tr1_out, need_result, rtol=1e-05, atol=1e-05)) elif col_type == "parallel_embedding": result_data = tr0_out[0] np.random.seed(2020) need_result = np.random.rand(10, 8) for i in range(result_data.shape[0]): for j in range(result_data.shape[1]): data = result_data[i][j] if data >= 4: data += 1 assert np.allclose( tr0_out[1][i][j], need_result[data], atol=1e-08) elif col_type == "row_parallel_linear": result_data = tr0_out[0] np.random.seed(2020) weight = np.random.rand(1000, 16) need_result = np.matmul(input1, weight) self.assertTrue( np.allclose( result_data, need_result, rtol=1e-05, atol=1e-05)) elif col_type == "column_parallel_linear": result_data = tr0_out[0] np.random.seed(2020) weight = np.random.rand(1000, 16) need_result = np.matmul(input1, weight) self.assertTrue( np.allclose( result_data, need_result, rtol=1e-05, atol=1e-05)) else: pass