Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 34bd0045 编写于 作者: M MrChengmo
浏览文件
操作

refine fleetrun.ps_launch

上级 f4c750d7
展开全部 显示空白变更内容
内联 并排
Showing with 580 addition and 245 deletion
paddle/fluid/operators/distributed/parameter_recv.cc
浏览文件 @ 34bd0045
......@@ -112,10 +112,6 @@ void RecvSelectedRows(const CommContext &rpc_ctx,
template <typename T>
void RecvLodTensor(const CommContext &rpc_ctx, const framework::Scope &scope) {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto cpu_place = platform::CPUPlace();
auto &cpu_ctx = *pool.Get(cpu_place);
distributed::RPCClient *rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>(rpc_ctx.trainer_id);
......@@ -125,10 +121,12 @@ void RecvLodTensor(const CommContext &rpc_ctx, const framework::Scope &scope) {
if (rpc_ctx.origin_varnames.size() == 1 &&
rpc_ctx.splited_varnames.size() == 1) {
auto varname = rpc_ctx.origin_varnames[0];
VLOG(4) << "recv " << varname << " from " << rpc_ctx.epmap[0];
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &ctx = *pool.Get(place);
VLOG(4) << "recv " << varname << " from " << rpc_ctx.epmap[0] << " in gpu? "
<< platform::is_gpu_place(place);
rets.push_back(rpc_client->AsyncGetVarNoBarrier(rpc_ctx.epmap[0], cpu_ctx,
scope, varname, varname));
for (size_t i = 0; i < rets.size(); i++) {
PADDLE_ENFORCE_NE(
rets[i]->Wait(), 0U,
......
python/paddle/distributed/fleet/base/role_maker.py
浏览文件 @ 34bd0045
......@@ -508,7 +508,7 @@ class RoleMakerBase(object):
and No.1 and No.3 cpu-trainer will work with No.1 gpu-trainerr
"""
assert self._heter_trainer_endpoints != []
return self._heter_trainer_endpoints[(self._current_id + 1) %
return self._heter_trainer_endpoints[(self._current_id) %
self._heter_worker_num()]
def _get_heter_worker_device(self):
......
python/paddle/distributed/fleet/launch.py
浏览文件 @ 34bd0045
......@@ -89,14 +89,40 @@ def _parse_args():
description='''start paddle training using multi-process mode.
see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
''')
base_group = parser.add_argument_group("Base Parameters")
base_group.add_argument(
"-d",
"--distributed_mode",
type=str,
choices=["collective", "ps", "ps_heter", "ps_gpu", ""],
default="",
help="Distributed running mode: collective/ps/ps_gpu/ps_heter")
base_group.add_argument(
"--log_dir",
type=str,
default="log",
help="The path for each process's log.If it's not set, the log will printed to default pipe."
)
base_group.add_argument(
"training_script",
type=str,
help="The full path to the single GPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script")
# Optional arguments for the launch helper
parser.add_argument(
# for collective
collective_group = parser.add_argument_group("Collective Parameters")
collective_group.add_argument(
"--ips",
type=str,
default="127.0.0.1",
help="Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..")
parser.add_argument(
collective_group.add_argument(
"--gpus",
type=str,
default=None,
......@@ -104,31 +130,30 @@ see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/tra
"each process is bound to a single GPU. And if it's not set, this module will use all the gpu cards for training."
)
parser.add_argument(
ps_group = parser.add_argument_group("Parameter-Server Parameters")
# for parameter server
ps_group.add_argument(
"--servers", type=str, default="", help="User defined servers ip:port")
parser.add_argument(
ps_group.add_argument(
"--workers", type=str, default="", help="User defined workers ip:port")
parser.add_argument("--worker_num", type=int, help="number of workers")
ps_group.add_argument(
"--heter_workers",
type=str,
default="",
help="User defined heter workers ip:port")
parser.add_argument("--server_num", type=int, help="number of servers")
ps_group.add_argument("--worker_num", type=int, help="number of workers")
ps_group.add_argument("--server_num", type=int, help="number of servers")
ps_group.add_argument(
"--heter_worker_num", type=int, help="number of heter_workers")
parser.add_argument(
"--log_dir",
ps_group.add_argument(
"--heter_worker_device",
type=str,
default="log",
help="The path for each process's log.If it's not set, the log will printed to default pipe."
)
# positional
parser.add_argument(
"training_script",
type=str,
help="The full path to the single GPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script")
default="gpu",
choices=["gpu", "xpu"],
help="heter worker device")
# rest from the training program
parser.add_argument('training_script_args', nargs=REMAINDER)
return parser.parse_args()
......@@ -246,209 +271,32 @@ def launch_collective(args):
def launch_ps(args):
ports = None
start_port = 6170
if args.server_num:
server_num = args.server_num
ports = get_ports(server_num, 0)
server_endpoints = ",".join(["127.0.0.1:" + str(x) for x in ports])
else:
assert args.servers != "", "The setting of CPU mode must be either server_num or servers."
server_endpoints = args.servers
server_endpoints_ips = [
x.strip().split(":")[0] for x in server_endpoints.split(",")
]
server_endpoints_port = [
x.strip().split(":")[1] for x in server_endpoints.split(",")
]
server_num = len(server_endpoints_ips)
cloud_flag = cloud_utils.use_paddlecloud()
if args.worker_num:
worker_num = args.worker_num
ports = get_ports(worker_num, server_num)
worker_endpoints = ",".join(["127.0.0.1:" + str(x) for x in ports])
else:
assert args.workers != "", "The setting of CPU mode must be either worker_num or workers."
worker_endpoints = args.workers
worker_endpoints_ips = [
x.strip().split(":")[0] for x in worker_endpoints.split(",")
]
worker_num = len(worker_endpoints_ips)
node_ips = list(set(server_endpoints_ips + worker_endpoints_ips))
worker_endpoints_len = [
len(x.strip().split(":")) for x in worker_endpoints.split(",")
]
if 1 in worker_endpoints_len:
# if no port value in worker_endpoints, will set default port values.
worker_endpoints_port = range(start_port + server_num,
start_port + server_num + worker_num, 1)
else:
worker_endpoints_port = [
x.strip().split(":")[1] for x in worker_endpoints.split(",")
]
# for ps-cpu on paddlecloud
direct_start_mode = ["ps", ""]
if cloud_flag and (args.distributed_mode in direct_start_mode):
direct_start(args)
return
elif cloud_flag and args.distributed_mode == "ps_heter":
cloud_ps_heter_env_set(args)
args.trainers = os.getenv("PADDLE_TRAINER_ENDPOINTS")
args.workers = os.getenv("PADDLE_PSERVERS_IP_PORT_LIST")
args.heter_workers = os.getenv("PADDLE_HETER_TRAINER_IP_PORT_LIST")
# local train
if len(set(node_ips)) == 1:
current_node_ip = node_ips[0]
else:
_, current_node_ip = get_host_name_ip()
assert current_node_ip in node_ips, "Can't find your local ip {%s} in args.servers and args.workers ips: {%s}" \
% (current_node_ip, node_ips)
node_rank = node_ips.index(current_node_ip)
logger.debug(
"parsed from args: node_ips:{} current_node_ip:{} node_rank:{}, server_ports:{}".
format(node_ips, current_node_ip, node_rank, server_endpoints_port))
cluster = Cluster(hdfs=None)
server_rank = 0
worker_rank = 0
for node_rank, ip in enumerate(node_ips):
pod = Pod()
pod.rank = node_rank
pod.addr = ip
for i in range(len(server_endpoints_ips)):
if ip == server_endpoints_ips[i]:
server = Trainer()
server.endpoint = "%s:%s" % (ip, server_endpoints_port[i])
server.rank = server_rank
server_rank += 1
pod.servers.append(server)
for j in range(len(worker_endpoints_ips)):
if ip == worker_endpoints_ips[j]:
worker = Trainer()
worker.endpoint = "%s:%s" % (ip, worker_endpoints_port[i])
worker.rank = worker_rank
worker_rank += 1
pod.workers.append(worker)
cluster.pods.append(pod)
pod_rank = node_ips.index(current_node_ip)
pod = cluster.pods[pod_rank]
default_env = os.environ.copy()
current_env = copy.copy(default_env)
gloo_rendezvous_dir = tempfile.mkdtemp()
# add gloo env
current_env["PADDLE_WITH_GLOO"] = "1"
current_env["PADDLE_GLOO_RENDEZVOUS"] = "2"
current_env["PADDLE_GLOO_FS_PATH"] = gloo_rendezvous_dir
current_env.pop("http_proxy", None)
current_env.pop("https_proxy", None)
procs = []
cmds = []
log_fns = []
for idx, cur_server in enumerate(pod.servers):
proc_env = {
"PADDLE_PSERVERS_IP_PORT_LIST": server_endpoints,
"PADDLE_TRAINER_ENDPOINTS": worker_endpoints,
"PADDLE_PORT": cur_server.endpoint.split(":")[1],
"TRAINING_ROLE": "PSERVER",
"PADDLE_TRAINERS_NUM": str(worker_num),
"POD_IP": cur_server.endpoint.split(":")[0],
"PADDLE_WITH_GLOO": "1"
}
current_env.update(proc_env)
cmd = [sys.executable, "-u", args.training_script
] + args.training_script_args
cmds.append(cmd)
if idx == 0:
logger.info(
"Local server start {} processes. First process distributed "
"environment info (Only For Debug): {}".format(
len(pod.servers),
pretty_print_envs(proc_env, ("Distributed Envs", "Value"))))
if args.log_dir is not None:
os.system("mkdir -p {}".format(args.log_dir))
fn = open("%s/serverlog.%d" % (args.log_dir, idx), "w")
log_fns.append(fn)
proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
else:
proc = subprocess.Popen(cmd, env=current_env)
tp = TrainerProc()
tp.proc = proc
tp.rank = cur_server.rank
tp.local_rank = idx
tp.log_fn = fn
tp.log_offset = fn.tell() if fn else None
tp.cmd = cmd
procs.append(tp)
for idx, cur_worker in enumerate(pod.workers):
proc_env = {
"PADDLE_PSERVERS_IP_PORT_LIST": server_endpoints,
"PADDLE_TRAINER_ENDPOINTS": worker_endpoints,
"PADDLE_TRAINERS_NUM": str(worker_num),
"TRAINING_ROLE": "TRAINER",
"PADDLE_TRAINER_ID": str(cur_worker.rank),
"PADDLE_WITH_GLOO": "1"
}
current_env.update(proc_env)
cmd = [sys.executable, "-u", args.training_script
] + args.training_script_args
cmds.append(cmd)
if idx == 0:
logger.info(
"Local worker start {} processes. First process distributed "
"environment info (Only For Debug): {}".format(
len(pod.workers),
pretty_print_envs(proc_env, ("Distributed Envs", "Value"))))
if args.log_dir is not None:
os.system("mkdir -p {}".format(args.log_dir))
fn = open("%s/workerlog.%d" % (args.log_dir, idx), "w")
log_fns.append(fn)
proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
else:
proc = subprocess.Popen(cmd, env=current_env)
tp = TrainerProc()
tp.proc = proc
tp.rank = cur_worker.rank
tp.local_rank = idx
tp.log_fn = fn
tp.log_offset = fn.tell() if fn else None
tp.cmd = cmd
procs.append(tp)
logger.info(
"Please check servers and workers logs in {}/workerlog.* and {}/serverlog.*".
format(args.log_dir, args.log_dir))
# only wait worker to finish here
for i, proc in enumerate(procs):
if i < len(pod.servers):
continue
procs[i].proc.wait()
if len(log_fns) > 0:
log_fns[i].close()
print("all workers exit, going to finish parameter server", file=sys.stderr)
for i in range(len(pod.servers)):
if len(log_fns) > 0:
log_fns[i].close()
procs[i].proc.terminate()
print("all parameter server are killed", file=sys.stderr)
if os.path.exists(gloo_rendezvous_dir):
shutil.rmtree(gloo_rendezvous_dir)
ps_launcher = ParameterServerLauncher(args)
ps_launcher.start_ps(args)
return
def launch():
args = _parse_args()
logger = get_logger()
_print_arguments(args)
ps_args = ['--worker_num', '--server_num', '--servers', '--workers']
ps_args = [
'--worker_num', '--server_num', '--heter_worker_num', '--servers',
'--workers', '--heter_worrkers', 'heter_worker_device'
]
collective_args = ['--ips', '--gpus']
has_ps_args = [
ps_arg for ps_arg in ps_args if ps_arg in " ".join(sys.argv[1:-1])
......@@ -462,9 +310,10 @@ def launch():
else:
cuda_device_num = 0
if len(has_ps_args) > 0 or cuda_device_num == 0:
ps_mode = ['ps', 'ps_gpu', 'ps_heter']
if len(has_ps_args) > 0 or args.distributed_mode in ps_mode:
logger.info(
"Run parameter-sever cpu mode. pserver arguments:{}, cuda count:{}".
"Run parameter-sever mode. pserver arguments:{}, cuda count:{}".
format(has_ps_args, cuda_device_num))
launch_ps(args)
elif len(has_collective_args) > 0:
......
python/paddle/distributed/fleet/meta_optimizers/parameter_server_optimizer.py
浏览文件 @ 34bd0045
......@@ -13,6 +13,7 @@
from paddle import fluid
from .meta_optimizer_base import MetaOptimizerBase
from ..base.private_helper_function import wait_server_ready
from paddle.fluid import core
import subprocess
import re
......@@ -74,6 +75,8 @@ class ParameterServerOptimizer(MetaOptimizerBase):
_startup = worker.delet_extra_optimizes_pass(_startup,
compiled_config)
compiled_config.set_origin_ps_main_program(_main)
compiled_config.set_origin_ps_startup_program(_startup)
# for heter program
if self.role_maker._is_heter_parameter_server_mode:
from paddle.fluid.incubate.fleet.parameter_server.ir import heter_trainer_pass as heter_worker
......@@ -91,6 +94,16 @@ class ParameterServerOptimizer(MetaOptimizerBase):
else:
_main = worker.append_send_ops_pass(_main, compiled_config)
_startup = _startup
compiled_config.set_origin_ps_main_program(_main)
compiled_config.set_origin_ps_startup_program(_startup)
# for trainer wait server ready
wait_server_ready(self.role_maker._get_pserver_endpoints())
# for ps-heter mode, wait heter worker ready
if self.role_maker._is_heter_parameter_server_mode and self.role_maker._is_worker(
):
wait_server_ready(self.role_maker._get_heter_worker_endpoints())
return _main, _startup
......
python/paddle/distributed/fleet/runtime/parameter_server_runtime.py
浏览文件 @ 34bd0045
......@@ -458,13 +458,13 @@ class ParameterServerRuntime(RuntimeBase):
def _save_distributed_persistables(self, executor, dirname, main_program):
dense_ctx = self.compiled_strategy.get_communicator_recv_context(
recv_type=1)
recv_type=1, use_origin_program=True)
sparse_ctx = self.compiled_strategy.get_communicator_recv_context(
recv_type=2)
recv_type=2, use_origin_program=True)
distributed_ctx = self.compiled_strategy.get_communicator_recv_context(
recv_type=3)
recv_type=3, use_origin_program=True)
recv_dense_varnames = self._save_dense_params(executor, dirname,
dense_ctx, main_program)
......@@ -516,7 +516,7 @@ class ParameterServerRuntime(RuntimeBase):
)
if main_program is None:
main_program = fluid.default_main_program()
main_program = self.compiled_strategy.get_origin_ps_main_program()
if isinstance(main_program, CompiledProgram):
raise TypeError(
......
python/paddle/fluid/incubate/fleet/parameter_server/ir/public.py
浏览文件 @ 34bd0045
......@@ -133,6 +133,8 @@ class CompileTimeStrategy(object):
self.origin_main_program = main_program
self.origin_startup_program = startup_program
self.origin_ps_main_program = main_program
self.origin_ps_startup_program = startup_program
self.strategy = strategy
self.role_maker = role_maker
......@@ -153,6 +155,11 @@ class CompileTimeStrategy(object):
self._build_var_distributed()
# for heter-ps save variables
self.origin_merged_variables_pairs = list(self.merged_variables_pairs)
self.origin_merged_dense_pairs = list(self.merged_dense_pairs)
self.origin_merged_sparse_pairs = list(self.merged_sparse_pairs)
def get_distributed_mode(self):
trainer = self.strategy.get_trainer_runtime_config()
return trainer.mode
......@@ -214,6 +221,18 @@ class CompileTimeStrategy(object):
def get_origin_startup_program(self):
return self.origin_startup_program
def set_origin_ps_main_program(self, program):
self.origin_ps_main_program = program
def set_origin_ps_startup_program(self, program):
self.origin_ps_startup_program = program
def get_origin_ps_main_program(self):
return self.origin_ps_main_program
def get_origin_ps_startup_program(self):
return self.origin_ps_startup_program
def get_sparse_varname_on_ps(self, is_distributed, endpoint=None):
if not endpoint:
endpoint = self.get_ps_endpoint()
......@@ -378,7 +397,9 @@ class CompileTimeStrategy(object):
send_ctx[name] = ctx
return send_ctx
def get_communicator_recv_context(self, recv_type=1):
def get_communicator_recv_context(self,
recv_type=1,
use_origin_program=False):
# recv_type
# 1 : DENSE 2. SPARSE 3. DISTRIBUTED 4. ALL
distibuted_varnames = get_sparse_tablenames(self.origin_main_program,
......@@ -392,7 +413,8 @@ class CompileTimeStrategy(object):
sparse_recv_ctx = {}
distributed_recv_ctx = {}
for merged in self.merged_variables_pairs:
variables_pairs = self.merged_variables_pairs if not use_origin_program else self.origin_merged_variables_pairs
for merged in variables_pairs:
params = merged[0]
if params.merged_var.name in sparse_varnames:
continue
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或