test_dist_base.py

#   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):
        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
        t = fluid.DistributeTranspiler(config=config)
        t.transpile(
            trainer_id=trainer_id,
            program=main_program,
            pservers=pserver_endpoints,
            trainers=trainers,
            sync_mode=sync_mode,
            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)
        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:
            place = fluid.CUDAPlace(0)
        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(
                len(pass_builder.all_passes()) - 3, "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_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
        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

    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
        # 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._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'}

        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)