# Copyright (c) 2018 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 time import unittest import os import sys import signal import subprocess import six import argparse import pickle import numpy as np import paddle.fluid as fluid from paddle.fluid import compiler RUN_STEP = 10 DEFAULT_BATCH_SIZE = 2 class TestDistRunnerBase(object): def get_model(self, batch_size=DEFAULT_BATCH_SIZE, lr=0.1, single_device=False, use_dgc=False): raise NotImplementedError( "get_model should be implemented by child classes.") @staticmethod def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers, sync_mode, dc_asgd=False, current_endpoint=None): # NOTE: import fluid until runtime, or else forking processes will cause error. config = fluid.DistributeTranspilerConfig() config.enable_dc_asgd = dc_asgd config.sync_mode = sync_mode # config.runtime_split_send_recv = True t = fluid.DistributeTranspiler(config=config) t.transpile( trainer_id=trainer_id, program=main_program, pservers=pserver_endpoints, trainers=trainers, current_endpoint=current_endpoint) return t def run_pserver(self, args): self.lr = args.lr self.get_model(batch_size=args.batch_size) # NOTE: pserver should not call memory optimize t = self.get_transpiler(args.trainer_id, fluid.default_main_program(), args.endpoints, args.trainers, args.sync_mode, args.dc_asgd) pserver_prog = t.get_pserver_program(args.current_endpoint) startup_prog = t.get_startup_program(args.current_endpoint, pserver_prog) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) exe.run(pserver_prog) def run_trainer(self, args): self.lr = args.lr if args.nccl2_reduce_layer_local_run: test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \ self.get_model(batch_size=args.batch_size, single_device=True) elif args.use_dgc: test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \ self.get_model(batch_size=args.batch_size, use_dgc=args.use_dgc) else: test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \ self.get_model(batch_size=args.batch_size) if args.mem_opt: fluid.memory_optimize(fluid.default_main_program(), skip_grads=True) if args.update_method == "pserver": t = self.get_transpiler(args.trainer_id, fluid.default_main_program(), args.endpoints, args.trainers, args.sync_mode, args.dc_asgd) trainer_prog = t.get_trainer_program() elif args.update_method == "nccl2" or args.update_method == "nccl2_reduce_layer": # transpile for nccl2 config = fluid.DistributeTranspilerConfig() config.mode = "nccl2" nccl2_t = fluid.DistributeTranspiler(config=config) nccl2_t.transpile( args.trainer_id, program=fluid.default_main_program(), startup_program=fluid.default_startup_program(), trainers=args.endpoints, current_endpoint=args.current_endpoint) trainer_prog = fluid.default_main_program() else: trainer_prog = fluid.default_main_program() if args.use_cuda: device_id = int(os.getenv("FLAGS_selected_gpus", "0")) place = fluid.CUDAPlace(device_id) else: place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_threads = 1 exec_strategy.allow_op_delay = False build_stra = fluid.BuildStrategy() # FIXME force disable enable_inplace and memory_optimize build_stra.enable_inplace = False build_stra.memory_optimize = False if args.use_reduce: build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce else: build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce pass_builder = None if args.batch_merge_repeat > 1: pass_builder = build_stra._finalize_strategy_and_create_passes() mypass = pass_builder.insert_pass(0, "multi_batch_merge_pass") mypass.set("num_repeats", args.batch_merge_repeat) if args.update_method == "nccl2" or args.update_method == "nccl2_reduce_layer": build_stra.num_trainers = len(args.endpoints.split(",")) build_stra.trainer_id = args.trainer_id else: # case args.update_method == "nccl2_reduce_layer": build_stra.num_trainers = 1 build_stra.trainer_id = 0 binary = compiler.CompiledProgram(trainer_prog).with_data_parallel( loss_name=avg_cost.name, build_strategy=build_stra, exec_strategy=exec_strategy) feed_var_list = [ var for var in trainer_prog.global_block().vars.values() if var.is_data ] feeder = fluid.DataFeeder(feed_var_list, place) reader_generator = train_reader() def get_data(): origin_batch = next(reader_generator) if args.update_method != "local" and args.use_reader_alloc: new_batch = [] for offset, item in enumerate(origin_batch): if offset % 2 == args.trainer_id: new_batch.append(item) return new_batch else: return origin_batch out_losses = [] for _ in six.moves.xrange(RUN_STEP): loss, = exe.run(binary, fetch_list=[avg_cost.name], feed=feeder.feed(get_data())) out_losses.append(loss[0]) if six.PY2: print(pickle.dumps(out_losses)) else: sys.stdout.buffer.write(pickle.dumps(out_losses)) def runtime_main(test_class): parser = argparse.ArgumentParser(description='Run dist test.') parser.add_argument( '--role', type=str, required=True, choices=['pserver', 'trainer']) parser.add_argument('--endpoints', type=str, required=False, default="") parser.add_argument( '--update_method', type=str, default="local", choices=["pserver", "nccl2", "local", "nccl2_reduce_layer"]) parser.add_argument('--trainer_id', type=int, required=False, default=0) parser.add_argument('--trainers', type=int, required=False, default=1) parser.add_argument( '--current_endpoint', type=str, required=False, default="") parser.add_argument('--sync_mode', action='store_true') parser.add_argument('--mem_opt', action='store_true') parser.add_argument('--use_cuda', action='store_true') parser.add_argument('--use_dgc', action='store_true') parser.add_argument('--use_reduce', action='store_true') parser.add_argument('--dc_asgd', action='store_true') parser.add_argument( '--use_reader_alloc', action='store_true', required=False) parser.add_argument('--batch_size', required=False, type=int, default=2) parser.add_argument('--lr', required=False, type=float, default=0.001) parser.add_argument( '--batch_merge_repeat', required=False, type=int, default=1) parser.add_argument( '--nccl2_reduce_layer_local_run', required=False, type=bool, default=False) args = parser.parse_args() model = test_class() if args.role == "pserver" and args.update_method == "pserver": model.run_pserver(args) else: model.run_trainer(args) import paddle.compat as cpt import socket from contextlib import closing class TestDistBase(unittest.TestCase): def _setup_config(self): raise NotImplementedError("tests should have _setup_config implemented") def _after_setup_config(self): if self._enforce_place == "CPU": self.__use_cuda = False self._use_dgc = False elif self._enforce_place == "GPU": self.__use_cuda = True else: if fluid.core.is_compiled_with_cuda(): self.__use_cuda = True else: self.__use_cuda = False self._use_dgc = False if self._use_reduce: assert not self._use_dgc def setUp(self): self._trainers = 2 self._pservers = 2 self._port_set = set() 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 self._sync_mode = True self._enforce_place = None self._mem_opt = False self._use_reduce = False self._dc_asgd = False # must use with async mode self._use_reader_alloc = True self._nccl2_mode = False self._mp_mode = False # FIXME(typhoonzero): I added this stupid argument to enable # testing allreduce layers, which users can call layers.allreduce # to accumulate tensors at anywhere. Find a better way to do this # test, reduce check this argument everywhere. self._nccl2_reduce_layer = False self._lr = 0.001 self._use_dgc = False self._setup_config() self._after_setup_config() 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 start_pserver(self, model_file, check_error_log, required_envs): ps0_ep, ps1_ep = self._ps_endpoints.split(",") ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --update_method pserver" ps0_cmd = ps_cmd % \ (self._python_interp, model_file, self._ps_endpoints, ps0_ep, self._trainers) ps1_cmd = ps_cmd % \ (self._python_interp, model_file, self._ps_endpoints, ps1_ep, self._trainers) if self._sync_mode: ps0_cmd += " --sync_mode" ps1_cmd += " --sync_mode" if self._mem_opt: ps0_cmd += " --mem_opt" ps1_cmd += " --mem_opt" print(ps0_cmd) print(ps1_cmd) ps0_pipe = open("/tmp/ps0_err.log", "wb") ps1_pipe = open("/tmp/ps1_err.log", "wb") ps0_proc = subprocess.Popen( ps0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps0_pipe, env=required_envs) ps1_proc = subprocess.Popen( ps1_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps1_pipe, env=required_envs) return ps0_proc, ps1_proc, ps0_pipe, ps1_pipe def _run_local(self, model, envs, check_error_log=False, batch_size=DEFAULT_BATCH_SIZE, batch_merge_repeat=1): cmd = "%s %s --role trainer --lr %f" % (self._python_interp, model, self._lr) if batch_size != DEFAULT_BATCH_SIZE: cmd += " --batch_size %d" % batch_size if batch_merge_repeat > 1: cmd += " --batch_merge_repeat %d" % batch_merge_repeat if self._nccl2_reduce_layer: cmd += " --nccl2_reduce_layer_local_run 1" if self.__use_cuda: cmd += " --use_cuda" env_local = { "CUDA_VISIBLE_DEVICES": "0", "PADDLE_TRAINERS_NUM": "1", "PADDLE_TRAINER_ID": "0" } else: env_local = {'CPU_NUM': '1'} env_local.update(envs) print("local_cmd: {}, env: {}".format(cmd, env_local)) if check_error_log: err_log = open("/tmp/trainer.err.log", "wb") local_proc = subprocess.Popen( cmd.split(" "), stdout=subprocess.PIPE, stderr=err_log, env=env_local) else: local_proc = subprocess.Popen( cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env_local) local_out, local_err = local_proc.communicate() if check_error_log: err_log.close() sys.stderr.write('local_stderr: %s\n' % local_err) sys.stderr.write('local_stdout: %s\n' % pickle.loads(local_out)) return pickle.loads(local_out) def _run_cluster(self, model, envs, check_error_log): # Run dist train to compare with local results ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model, check_error_log, envs) ps0_ep, ps1_ep = self._ps_endpoints.split(",") tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --trainers %d --update_method pserver --lr %f" tr0_cmd = tr_cmd % \ (self._python_interp, model, self._ps_endpoints, 0, ps0_ep, self._trainers, self._lr) tr1_cmd = tr_cmd % \ (self._python_interp, model, self._ps_endpoints, 1, ps1_ep, self._trainers, self._lr) if self._sync_mode: tr0_cmd += " --sync_mode" tr1_cmd += " --sync_mode" if self._mem_opt: tr0_cmd += " --mem_opt" tr1_cmd += " --mem_opt" if self._use_reduce: tr0_cmd += " --use_reduce" tr1_cmd += " --use_reduce" if self._use_reader_alloc: tr0_cmd += " --use_reader_alloc" tr1_cmd += " --use_reader_alloc" if self.__use_cuda: tr0_cmd += " --use_cuda" tr1_cmd += " --use_cuda" env0 = {"CUDA_VISIBLE_DEVICES": "0"} env1 = {"CUDA_VISIBLE_DEVICES": "1"} else: env0 = {'CPU_NUM': '1'} env1 = {'CPU_NUM': '1'} env0.update(envs) env1.update(envs) print("tr0_cmd: {}, env: {}".format(tr0_cmd, env0)) print("tr1_cmd: {}, env: {}".format(tr1_cmd, env1)) tr0_pipe = open("/tmp/tr0_err.log", "wb") tr1_pipe = open("/tmp/tr1_err.log", "wb") tr0_proc = subprocess.Popen( tr0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=tr0_pipe, env=env0) tr1_proc = subprocess.Popen( tr1_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=tr1_pipe, env=env1) # Wait until trainer process terminate while True: stat0 = tr0_proc.poll() time.sleep(0.1) if stat0 is not None: break while True: stat1 = tr1_proc.poll() time.sleep(0.1) if stat1 is not None: break tr0_out, tr0_err = tr0_proc.communicate() tr1_out, tr1_err = tr1_proc.communicate() # close trainer file tr0_pipe.close() tr1_pipe.close() ps0_pipe.close() ps1_pipe.close() ps0.terminate() ps1.terminate() # print server log with open("/tmp/ps0_err.log", "r") as fn: sys.stderr.write("ps0 stderr: %s\n" % fn.read()) with open("/tmp/ps1_err.log", "r") as fn: sys.stderr.write("ps1 stderr: %s\n" % fn.read()) # print log with open("/tmp/tr0_err.log", "r") as fn: sys.stderr.write('trainer 0 stderr: %s\n' % fn.read()) with open("/tmp/tr1_err.log", "r") as fn: sys.stderr.write('trainer 1 stderr: %s\n' % fn.read()) return pickle.loads(tr0_out), pickle.loads(tr1_out) def _run_cluster_nccl2(self, model, envs, nccl2_reduce_layer, check_error_log): # NOTE: we reuse ps_endpoints as nccl2 worker endpoints worker_endpoints = self._ps_endpoints.split(",") w0_ep, w1_ep = worker_endpoints if nccl2_reduce_layer: update_method = "nccl2_reduce_layer" else: update_method = "nccl2" tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --update_method %s --lr %f" tr0_cmd = tr_cmd % \ (self._python_interp, model, self._ps_endpoints, 0, w0_ep, update_method, self._lr) tr1_cmd = tr_cmd % \ (self._python_interp, model, self._ps_endpoints, 1, w1_ep, update_method, self._lr) if self._mem_opt: tr0_cmd += " --mem_opt" tr1_cmd += " --mem_opt" if self._use_reduce: tr0_cmd += " --use_reduce" tr1_cmd += " --use_reduce" if self._use_reader_alloc: tr0_cmd += " --use_reader_alloc" tr1_cmd += " --use_reader_alloc" if self.__use_cuda: tr0_cmd += " --use_cuda" tr1_cmd += " --use_cuda" env0 = { "CUDA_VISIBLE_DEVICES": "0", # for test nccl2 layer "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ID": "0" } env1 = { "CUDA_VISIBLE_DEVICES": "1", "PADDLE_TRAINERS_NUM": "2", "PADDLE_TRAINER_ID": "1" } else: env0 = {'CPU_NUM': '1'} env1 = {'CPU_NUM': '1'} if self._use_dgc: tr0_cmd += " --use_dgc" tr1_cmd += " --use_dgc" if self._mp_mode: env0 = {"FLAGS_selected_gpus": "0"} env1 = {"FLAGS_selected_gpus": "1"} env0.update(envs) env1.update(envs) print("tr0_cmd:{}, env: {}".format(tr0_cmd, env0)) print("tr1_cmd:{}, env: {}".format(tr1_cmd, env1)) tr0_pipe = open("/tmp/tr0_err.log", "wb") tr1_pipe = open("/tmp/tr1_err.log", "wb") tr0_proc = subprocess.Popen( tr0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=tr0_pipe, env=env0) tr1_proc = subprocess.Popen( tr1_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() # close trainer file tr0_pipe.close() tr1_pipe.close() # print log sys.stderr.write('trainer 0 stderr: %s\n' % tr0_err) sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err) return pickle.loads(tr0_out), pickle.loads(tr1_out) def check_with_place(self, model_file, delta=1e-3, check_error_log=False, need_envs={}): # TODO(typhoonzero): should auto adapt GPU count on the machine. required_envs = { "PATH": os.getenv("PATH", ""), "PYTHONPATH": os.getenv("PYTHONPATH", ""), "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""), "FLAGS_fraction_of_gpu_memory_to_use": "0.15", "FLAGS_rpc_deadline": "5000", # 5sec to fail fast "FLAGS_cudnn_deterministic": "1", "http_proxy": "", "NCCL_P2P_DISABLE": "1" } required_envs.update(need_envs) if check_error_log: required_envs["GLOG_v"] = "3" required_envs["GLOG_logtostderr"] = "1" local_losses\ = self._run_local(model_file, required_envs, check_error_log) if self._nccl2_mode: if self._nccl2_reduce_layer: tr0_losses, tr1_losses = self._run_cluster_nccl2( model_file, required_envs, True, check_error_log) else: tr0_losses, tr1_losses = self._run_cluster_nccl2( model_file, required_envs, False, check_error_log) else: tr0_losses, tr1_losses = self._run_cluster( model_file, required_envs, check_error_log) for step_id in range(RUN_STEP): local_loss = local_losses[step_id] tr0_loss = tr0_losses[step_id] tr1_loss = tr1_losses[step_id] dist_loss = (np.array([tr0_loss]) + np.array([tr1_loss])) / 2 print("=======", local_loss, ":", dist_loss[0], "=======") self.assertAlmostEqual(local_loss, dist_loss[0], delta=delta)