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未验证 提交 371f377b 编写于 作者: X xujiaqi01 提交者： GitHub
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add GeneralRoleMaker (#22295) 

* add GeneralRoleMaker which is for general usage
* test=develop
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浏览文件 @ 371f377b
......@@ -219,6 +219,13 @@ RUN wget -q https://launchpad.net/ubuntu/+archive/primary/+sourcefiles/binutils/
cd binutils-2.27 && \
./configure && make -j && make install && cd .. && rm -rf binutils-2.27 binutils_2.27.orig.tar.gz
RUN wget --no-check-certificate https://pslib.bj.bcebos.com/openmpi-1.4.5.tar.gz && tar -xzf openmpi-1.4.5.tar.gz && \
cd openmpi-1.4.5 && ./configure --prefix=/usr/local && make all -j8 && make install -j8 && \
export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH && export PATH=/usr/local/bin:$PATH && cd .. && \
rm -rf openmpi-1.4.5.tar.gz && pip --no-cache-dir install mpi4py && ln -fs /bin/bash /bin/sh && \
apt-get install libprotobuf-dev -y
RUN pip --no-cache-dir install -U netifaces==0.10.9
# Older versions of patchelf limited the size of the files being processed and were fixed in this pr.
# https://github.com/NixOS/patchelf/commit/ba2695a8110abbc8cc6baf0eea819922ee5007fa
# So install a newer version here.
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 371f377b
......@@ -214,6 +214,7 @@ cc_library(parallel_executor SRCS parallel_executor.cc DEPS
graph build_strategy
fast_threaded_ssa_graph_executor variable_helper)
cc_test(dist_multi_trainer_test SRCS dist_multi_trainer_test.cc DEPS executor)
cc_library(prune SRCS prune.cc DEPS framework_proto boost)
cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry
......
paddle/fluid/framework/data_set.cc
浏览文件 @ 371f377b
......@@ -287,6 +287,7 @@ void DatasetImpl<T>::LocalShuffle() {
template <typename T>
void DatasetImpl<T>::GlobalShuffle(int thread_num) {
#ifdef PADDLE_WITH_PSLIB
VLOG(3) << "DatasetImpl<T>::GlobalShuffle() begin";
platform::Timer timeline;
timeline.Start();
......@@ -379,6 +380,7 @@ void DatasetImpl<T>::GlobalShuffle(int thread_num) {
timeline.Pause();
VLOG(3) << "DatasetImpl<T>::GlobalShuffle() end, cost time="
<< timeline.ElapsedSec() << " seconds";
#endif
}
template <typename T>
......
paddle/fluid/framework/dist_multi_trainer.cc
浏览文件 @ 371f377b
......@@ -41,8 +41,8 @@ void DistMultiTrainer::Initialize(const TrainerDesc &trainer_desc,
need_dump_field_ = false;
}
}
mpi_rank_ = trainer_desc.mpi_rank() / 2;
mpi_size_ = trainer_desc.mpi_size() / 2;
mpi_rank_ = trainer_desc.mpi_rank();
mpi_size_ = trainer_desc.mpi_size();
dump_file_num_ = trainer_desc.dump_file_num();
const std::vector<paddle::framework::DataFeed *> readers =
dataset->GetReaders();
......
paddle/fluid/framework/dist_multi_trainer_test.cc 0 → 100644
浏览文件 @ 371f377b
// 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.
#include <fstream>
#include <iostream>
#include <sstream>
#include "google/protobuf/io/zero_copy_stream_impl.h"
#include "google/protobuf/message.h"
#include "google/protobuf/text_format.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/trainer.h"
#if defined _WIN32 || defined __APPLE__
#else
#define _LINUX
#endif
namespace paddle {
namespace framework {
TEST(DisMultiTrainerTest, test1) {
#ifdef _LINUX
std::shared_ptr<DistMultiTrainer> tmp1 = std::make_shared<DistMultiTrainer>();
TrainerDesc t;
t.set_class_name("DistMultiTrainer");
t.set_device_worker_name("DownpourWorker");
t.set_thread_num(1);
auto* m = t.mutable_downpour_param()->add_program_config();
m->set_program_id("123");
std::string str;
str += "name: \"MultiSlotDataFeed\"\nbatch_size: 2\nmulti_slot_desc {\n";
str += "slots {\nname: \"words\"\ntype: \"uint64\"\nis_dense: false\n";
str += "is_used: true\n}\nslots {\nname: \"label\"\ntype: \"uint64\"\n";
str += "is_dense: false\nis_used: true\n}\n}\n";
std::shared_ptr<MultiSlotDataset> dataset =
std::make_shared<MultiSlotDataset>();
dataset->SetFileList(std::vector<std::string>());
dataset->SetThreadNum(1);
dataset->SetTrainerNum(1);
dataset->SetDataFeedDesc(str);
dataset->CreateReaders();
tmp1->Initialize(t, dataset.get());
#endif
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/fleet/gloo_wrapper.cc
浏览文件 @ 371f377b
......@@ -21,6 +21,7 @@ HdfsStore::HdfsStore(const std::string& path) {
path_ = path;
wait_sleep_ms_ = 3000;
wait_timeout_ = std::chrono::seconds(999999999);
retry_times_ = 100;
}
void HdfsStore::set(const std::string& key, const std::vector<char>& data) {
......@@ -33,10 +34,27 @@ void HdfsStore::set(const std::string& key, const std::vector<char>& data) {
paddle::framework::fs_remove(path);
}
int err_no = 0;
std::shared_ptr<FILE> fp = paddle::framework::fs_open_write(tmp, &err_no, "");
size_t write_count = fwrite_unlocked(data.data(), 1, data.size(), fp.get());
VLOG(3) << "HdfsStore::set write_count=" << write_count << " key " << key;
fp.reset();
for (int i = 1; i <= retry_times_; ++i) {
std::shared_ptr<FILE> fp =
paddle::framework::fs_open_write(tmp, &err_no, "");
if (err_no != 0) {
VLOG(0) << "fs_open_write failed, retry times " << i << " err no "
<< err_no;
fp.reset();
sleep(wait_sleep_ms_ / 1000);
continue;
}
size_t write_count = fwrite_unlocked(data.data(), 1, data.size(), fp.get());
if (write_count != data.size()) {
VLOG(0) << "fwrite_unlocked failed, retry times " << i << " write_count "
<< write_count << " data.size() " << data.size();
fp.reset();
sleep(2);
continue;
}
fp.reset();
break;
}
paddle::framework::fs_mv(tmp, path);
#endif
}
......@@ -131,7 +149,7 @@ void GlooWrapper::Init(int rank, int size, const std::string& path,
}
rank_ = rank;
size_ = size;
std::string cmd = std::string("hadoop fs");
std::string cmd = std::string("${HADOOP_HOME}/bin/hadoop fs");
cmd += " -D fs.default.name=" + fs_name;
cmd += " -D hadoop.job.ugi=" + fs_ugi;
paddle::framework::hdfs_set_command(cmd);
......@@ -149,16 +167,19 @@ void GlooWrapper::Init(int rank, int size, const std::string& path,
is_initialized_ = true;
}
template void GlooWrapper::AllReduce<int64_t>(
template std::vector<int64_t> GlooWrapper::AllReduce<int64_t>(
std::vector<int64_t>& sendbuf, // NOLINT
std::vector<int64_t>& recvbuf, // NOLINT
const std::string& mode);
template void GlooWrapper::AllReduce<double>(
template std::vector<double> GlooWrapper::AllReduce<double>(
std::vector<double>& sendbuf, // NOLINT
std::vector<double>& recvbuf, // NOLINT
const std::string& mode);
template std::vector<uint64_t> GlooWrapper::AllReduce<uint64_t>(
std::vector<uint64_t>& sendbuf, // NOLINT
const std::string& mode);
template std::vector<int64_t> GlooWrapper::AllGather<int64_t>(
int64_t& input); // NOLINT
template std::vector<uint64_t> GlooWrapper::AllGather<uint64_t>(
uint64_t& input); // NOLINT
template std::vector<double> GlooWrapper::AllGather<double>(
double& input); // NOLINT
......
paddle/fluid/framework/fleet/gloo_wrapper.h
浏览文件 @ 371f377b
......@@ -70,6 +70,7 @@ class HdfsStore {
std::string path_;
int wait_sleep_ms_;
std::chrono::seconds wait_timeout_;
int retry_times_;
};
} // namespace rendezvous
......@@ -107,9 +108,10 @@ class GlooWrapper {
}
template <typename T>
void AllReduce(std::vector<T>& sendbuf, std::vector<T>& recvbuf, // NOLINT
const std::string& mode = "sum") {
std::vector<T> AllReduce(std::vector<T>& sendbuf, // NOLINT
const std::string& mode = "sum") { // NOLINT
CHECK_EQ(is_initialized_, true);
std::vector<T> recvbuf(sendbuf.size(), T());
CHECK_EQ(sendbuf.size() == recvbuf.size(), true);
#ifdef PADDLE_WITH_GLOO
gloo::AllreduceOptions opts(context_);
......@@ -133,6 +135,7 @@ class GlooWrapper {
}
gloo::allreduce(opts);
#endif
return recvbuf;
}
template <typename T>
......
paddle/fluid/framework/fleet/test_fleet.cc
浏览文件 @ 371f377b
......@@ -49,8 +49,7 @@ TEST(TEST_GLOO, store_1) {
gw.Size();
gw.Barrier();
std::vector<double> input;
std::vector<double> output;
gw.AllReduce(input, output);
gw.AllReduce(input);
int64_t t;
gw.AllGather(t);
#endif
......
paddle/fluid/pybind/gloo_wrapper_py.cc
浏览文件 @ 371f377b
......@@ -37,12 +37,12 @@ void BindGlooWrapper(py::module* m) {
.def("rank", &framework::GlooWrapper::Rank)
.def("size", &framework::GlooWrapper::Size)
.def("barrier", &framework::GlooWrapper::Barrier)
.def("all_reduce", &framework::GlooWrapper::AllReduce<uint64_t>)
.def("all_reduce", &framework::GlooWrapper::AllReduce<int64_t>)
.def("all_reduce", &framework::GlooWrapper::AllReduce<double>)
.def("all_gather", &framework::GlooWrapper::AllGather<uint64_t>)
.def("all_gather", &framework::GlooWrapper::AllGather<int64_t>)
.def("all_gather", &framework::GlooWrapper::AllGather<double>)
.def("Allreduce", &framework::GlooWrapper::AllReduce<int64_t>)
.def("Allreduce", &framework::GlooWrapper::AllReduce<double>);
.def("all_gather", &framework::GlooWrapper::AllGather<double>);
} // end BindGlooWrapper
} // end namespace pybind
} // end namespace paddle
python/paddle/fluid/dataset.py
浏览文件 @ 371f377b
......@@ -526,7 +526,7 @@ class InMemoryDataset(DatasetBase):
"""
trainer_num = 1
if fleet is not None:
fleet._role_maker._barrier_worker()
fleet._role_maker.barrier_worker()
trainer_num = fleet.worker_num()
if self.fleet_send_batch_size is None:
self.fleet_send_batch_size = 1024
......@@ -537,14 +537,14 @@ class InMemoryDataset(DatasetBase):
self.dataset.set_fleet_send_batch_size(self.fleet_send_batch_size)
self.dataset.set_fleet_send_sleep_seconds(self.fleet_send_sleep_seconds)
if fleet is not None:
fleet._role_maker._barrier_worker()
fleet._role_maker.barrier_worker()
self.dataset.global_shuffle(thread_num)
if fleet is not None:
fleet._role_maker._barrier_worker()
fleet._role_maker.barrier_worker()
if self.merge_by_lineid:
self.dataset.merge_by_lineid()
if fleet is not None:
fleet._role_maker._barrier_worker()
fleet._role_maker.barrier_worker()
def release_memory(self):
"""
......@@ -599,8 +599,8 @@ class InMemoryDataset(DatasetBase):
local_data_size = np.array([local_data_size])
if fleet is not None:
global_data_size = local_data_size * 0
fleet._role_maker._node_type_comm.Allreduce(local_data_size,
global_data_size)
fleet._role_maker.all_reduce_worker(local_data_size,
global_data_size)
return global_data_size[0]
return local_data_size[0]
......@@ -637,8 +637,8 @@ class InMemoryDataset(DatasetBase):
local_data_size = np.array([local_data_size])
if fleet is not None:
global_data_size = local_data_size * 0
fleet._role_maker._node_type_comm.Allreduce(local_data_size,
global_data_size)
fleet._role_maker.all_reduce_worker(local_data_size,
global_data_size)
return global_data_size[0]
return local_data_size[0]
......
python/paddle/fluid/incubate/fleet/base/fleet_base.py
浏览文件 @ 371f377b
......@@ -202,6 +202,22 @@ class Fleet(object):
self._role_maker.generate_role()
self._is_initialized = True
def all_reduce_worker(self, input, output):
"""
all reduce between workers, only support array of one dim.
Args:
input(list|numpy.array): array of one dim
output(list|numpy.array): array of one dim
"""
self._role_maker.all_reduce_worker(input, output)
def barrier_worker(self):
"""
barrier between workers
"""
self._role_maker.barrier_worker()
@abc.abstractmethod
def init_worker(self):
pass
......
python/paddle/fluid/incubate/fleet/base/role_maker.py
浏览文件 @ 371f377b
......@@ -11,16 +11,18 @@
# 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.
"""Defination of Role Makers."""
from __future__ import print_function
import paddle.fluid as fluid
import os
import time
__all__ = [
'Role', 'RoleMakerBase', 'MPISymetricRoleMaker', 'UserDefinedRoleMaker',
'UserDefinedCollectiveRoleMaker', 'PaddleCloudRoleMaker'
'UserDefinedCollectiveRoleMaker', 'PaddleCloudRoleMaker', 'GeneralRoleMaker'
]
import os
class Role:
WORKER = 1
......@@ -107,6 +109,43 @@ class RoleMakerBase(object):
self._role, self._current_id, self._worker_endpoints,
self._server_endpoints)
def all_gather(self, input):
"""
all gather between trainers and pservers
Args:
input(int|float): input value
Returns:
return a list of values
"""
print("warning: RoleMakerBase does not have all gather.")
return None
def all_reduce_worker(self, input, output, mode="sum"):
"""
all reduce between trainers if current role is TRAINER,
only support array of one dim.
Args:
input(list/numpy.array): array of one dim
output(list/numpy.array): array of one dim
mode(str): "sum" or "min" or "max"
"""
print("warning: RoleMakerBase does not have all reduce worker.")
def barrier_worker(self):
"""
barrier between trainers if current role is TRAINER
"""
print("warning: RoleMakerBase does not have barrier worker.")
def barrier_all(self):
"""
barrier between trainers if current role is PSERVER
"""
print("warning: RoleMakerBase does not have barrier all.")
class MPIRoleMaker(RoleMakerBase):
"""
......@@ -115,6 +154,7 @@ class MPIRoleMaker(RoleMakerBase):
"""
def __init__(self):
"""Init."""
super(MPIRoleMaker, self).__init__()
from mpi4py import MPI
self.MPI = MPI
......@@ -124,16 +164,12 @@ class MPIRoleMaker(RoleMakerBase):
self._ip = None
def _get_rank(self):
"""
return rank
"""
"""Return rank."""
self._rank = self._comm.Get_rank()
return self._rank
def _get_size(self):
"""
return size
"""
"""Return size."""
self._size = self._comm.Get_size()
return self._size
......@@ -174,9 +210,7 @@ class MPIRoleMaker(RoleMakerBase):
return self._ips
def get_local_ip(self):
"""
return get local ip
"""
"""Return get local ip."""
import socket
self._ip = socket.gethostbyname(socket.gethostname())
return self._ip
......@@ -196,16 +230,68 @@ class MPISymetricRoleMaker(MPIRoleMaker):
"""
def __init__(self):
"""Init."""
super(MPISymetricRoleMaker, self).__init__()
self._node_type = None
self._proc_per_node = 2
self._pserver_rand_port = 0
def _check_role_generation(self):
"""Check whether role has been generated."""
if not self._role_is_generated:
raise NameError("generate_role() should be called first")
return True
def all_gather(self, input):
"""
all gather between trainers and pservers
Args:
input(int|float): input value
Returns:
return a list of values
"""
if not self._role_is_generated:
self.generate_role()
return self._all_gather(input)
def all_reduce_worker(self, input, output, mode="sum"):
"""
all reduce between trainers if current role is TRAINER,
only support array of one dim.
Args:
input(list/numpy.array): array of one dim
output(list/numpy.array): array of one dim
mode(str): "sum" or "min" or "max"
"""
if not self._role_is_generated:
self.generate_role()
if not self.is_worker():
print("warning: current role is not worker in all_reduce_worker")
return
self._all_reduce(input, output, mode)
def barrier_worker(self):
"""
barrier between trainers if current role is TRAINER
"""
if not self._role_is_generated:
self.generate_role()
if self.is_worker():
self._node_type_comm.barrier()
else:
print("warning: current role is not worker in barrier_worker")
def barrier_all(self):
"""
barrier between trainers if current role is PSERVER
"""
if not self._role_is_generated:
self.generate_role()
self._comm.barrier()
def is_first_worker(self):
"""
return whether current process is the first worker assigned by role maker
......@@ -215,6 +301,12 @@ class MPISymetricRoleMaker(MPIRoleMaker):
return False
def get_pserver_endpoints(self):
"""
get pserver endpoints
Returns:
endpoints(list): pserver endpoints
"""
if self._pserver_rand_port <= 0:
import random
random.seed(self._server_num())
......@@ -285,6 +377,28 @@ class MPISymetricRoleMaker(MPIRoleMaker):
self.generate_role()
return self._get_size() / self._proc_per_node
def _all_reduce(self, input, output, mode="sum"):
"""
all reduce between trainers if current role is TRAINER,
only support array of one dim.
Args:
input(list/numpy.array): array of one dim
output(list/numpy.array): array of one dim
mode(str): "sum" or "min" or "max"
"""
if not self._role_is_generated:
self.generate_role()
if mode == "sum":
mode = self.MPI.SUM
elif mode == "max":
mode = self.MPI.MAX
elif mode == "min":
mode = self.MPI.MIN
else:
raise ValueError("unknown mode: %s" % mode)
self._node_type_comm.Allreduce(input, output, op=mode)
def _barrier_worker(self):
"""
barrier all workers in current distributed job
......@@ -325,12 +439,18 @@ class MPISymetricRoleMaker(MPIRoleMaker):
class PaddleCloudRoleMaker(RoleMakerBase):
"""
role maker for paddle cloud,
base class is RoleMakerBase
"""
def __init__(self, is_collective=False):
super(PaddleCloudRoleMaker, self).__init__()
self._role_is_generated = False
self._is_collective = is_collective
def generate_role(self):
"""Generate role."""
if not self._role_is_generated:
if not self._is_collective:
try:
......@@ -419,17 +539,352 @@ class PaddleCloudRoleMaker(RoleMakerBase):
return self._trainers_num
class GeneralRoleMaker(RoleMakerBase):
"""
This role maker is for general use, you can set os.environ to customize:
PADDLE_PSERVERS_IP_PORT_LIST : all pservers' ip:port, seperated by ','
PADDLE_TRAINER_ENDPOINTS : all trainers' ip:port, seperated by ','
TRAINING_ROLE : TRAINER or PSERVER
PADDLE_TRAINER_ID : current trainer id (only for trainer),
it is index in PADDLE_TRAINER_ENDPOINTS
PADDLE_PSERVER_ID : current pserver id (only for pserver)
it is index in PADDLE_PSERVERS_IP_PORT_LIST
"""
def __init__(self, **kwargs):
super(RoleMakerBase, self).__init__()
self._role_is_generated = False
self._hdfs_name = kwargs.get("hdfs_name", "")
self._hdfs_ugi = kwargs.get("hdfs_ugi", "")
self._hdfs_path = kwargs.get("path", "")
self._iface = self.__get_default_iface()
# this environment variable can be empty
self._prefix = os.getenv("SYS_JOB_ID", "")
def generate_role(self):
"""
generate role for general role maker
"""
if not self._role_is_generated:
eplist = os.environ["PADDLE_PSERVERS_IP_PORT_LIST"].split(",")
training_role = os.environ["TRAINING_ROLE"]
worker_endpoints = os.environ["PADDLE_TRAINER_ENDPOINTS"].split(",")
trainers_num = len(worker_endpoints)
if training_role not in ["TRAINER", "PSERVER"]:
raise ValueError("TRAINING_ROLE must be PSERVER or TRAINER")
if training_role == "TRAINER":
role = Role.WORKER
current_id = int(os.environ["PADDLE_TRAINER_ID"])
self._node_type = 1
self._cur_endpoint = worker_endpoints[current_id]
gloo = fluid.core.Gloo()
gloo.init(current_id,
len(worker_endpoints),
self._hdfs_path.rstrip("/") + "/trainer",
self._hdfs_name, self._hdfs_ugi, self._iface,
self._prefix)
self._node_type_comm = gloo
elif training_role == "PSERVER":
role = Role.SERVER
if os.environ.get("PADDLE_PSERVER_ID") is not None:
current_id = int(os.environ["PADDLE_PSERVER_ID"])
cur_endpoint = eplist[current_id]
else:
# this is for compatible with paddlecloud
cur_ip = os.environ["POD_IP"]
cur_port = os.environ["PADDLE_PORT"]
cur_endpoint = ":".join([cur_ip, cur_port])
current_id = eplist.index(cur_endpoint)
self._node_type = 0
self._cur_endpoint = cur_endpoint
gloo = fluid.core.Gloo()
gloo.init(current_id,
len(eplist),
self._hdfs_path.rstrip("/") + "/pserver",
self._hdfs_name, self._hdfs_ugi, self._iface,
self._prefix)
self._node_type_comm = gloo
gloo = fluid.core.Gloo()
all_list = worker_endpoints + eplist
gloo.init(
all_list.index(self._cur_endpoint),
len(all_list),
self._hdfs_path.rstrip("/") + "/all", self._hdfs_name,
self._hdfs_ugi, self._iface, self._prefix)
self._all_comm = gloo
self._trainers_num = trainers_num
self._server_endpoints = eplist
self._role = role
self._current_id = current_id
self._rank = all_list.index(self._cur_endpoint)
self._size = len(all_list)
self._worker_endpoints = worker_endpoints
self._role_is_generated = True
def all_gather(self, input):
"""
all gather between trainers and pservers
Args:
input(int|float): input value
Returns:
return a list of values
"""
return self._all_gather(input)
def all_reduce_worker(self, input, output, mode="sum"):
"""
all reduce between trainers if current role is TRAINER,
only support array of one dim.
Args:
input(list/numpy.array): array of one dim
output(list/numpy.array): array of one dim
mode(str): "sum" or "min" or "max"
"""
if not self.is_worker():
return
self._all_reduce(input, output, mode)
def barrier_worker(self):
"""
barrier between trainers if current role is TRAINER
"""
self._barrier_worker()
def barrier_all(self):
"""
barrier between trainers if current role is PSERVER
"""
self._barrier_all()
def get_local_endpoint(self):
"""
get local endpoint of current process
"""
if not self._role_is_generated:
self.generate_role()
return self._cur_endpoint
def get_trainer_endpoints(self):
"""
get endpoint of all trainers
"""
if not self._role_is_generated:
self.generate_role()
return self._worker_endpoints
def get_pserver_endpoints(self):
"""
get endpoint of all pservers
"""
if not self._role_is_generated:
self.generate_role()
return self._server_endpoints
def is_worker(self):
"""
whether current process is worker
"""
if not self._role_is_generated:
self.generate_role()
return self._role == Role.WORKER
def is_server(self):
"""
whether current process is server
"""
if not self._role_is_generated:
self.generate_role()
return self._role == Role.SERVER
def is_first_worker(self):
"""
whether current process is worker of rank 0
"""
if not self._role_is_generated:
self.generate_role()
return self._role == Role.WORKER and self._current_id == 0
def worker_index(self):
"""
get index of current worker
"""
if not self._role_is_generated:
self.generate_role()
return self._current_id
def server_index(self):
"""
get index of current server
"""
if not self._role_is_generated:
self.generate_role()
return self._current_id
def worker_num(self):
"""
retrun the current number of worker
"""
if not self._role_is_generated:
self.generate_role()
return self._worker_num()
def server_num(self):
"""
return the current number of server
"""
if not self._role_is_generated:
self.generate_role()
return self._server_num()
def _barrier_worker(self):
"""
barrier all workers in current distributed job
"""
if not self._role_is_generated:
self.generate_role()
if self.is_worker():
self._node_type_comm.barrier()
def _barrier_all(self):
"""
barrier all workers and servers in current distributed job
"""
if not self._role_is_generated:
self.generate_role()
self._all_comm.barrier()
def _barrier_server(self):
"""
barrier all servers in current distributed job
"""
if not self._role_is_generated:
self.generate_role()
if self.is_server():
self._node_type_comm.barrier()
def _worker_num(self):
"""
return the current number of worker
"""
if not self._role_is_generated:
self.generate_role()
return self._trainers_num
def _server_num(self):
"""
return the current number of server
"""
if not self._role_is_generated:
self.generate_role()
return len(self._server_endpoints)
def _finalize(self):
"""Default do nothing."""
pass
def _all_reduce(self, input, output, mode="sum"):
"""
all reduce between all workers
Args:
input(list|numpy.array): array of one dim
output(list|numpy.array): array of one dim
mode(str): "sum" or "min" or "max"
"""
if not self._role_is_generated:
self.generate_role()
input_list = [i for i in input]
ans = self._node_type_comm.all_reduce(input_list, mode)
for i in range(len(ans)):
output[i] = ans[i]
def _all_gather(self, obj):
"""
gather between all workers and pservers
"""
if not self._role_is_generated:
self.generate_role()
self._barrier_all()
return self._all_comm.all_gather(obj)
def _worker_gather(self, obj):
"""
gather between all workers
"""
if not self._role_is_generated:
self.generate_role()
if not self.is_worker():
return None
self._barrier_worker()
return self._node_type_comm.all_gather(obj)
def _get_rank(self):
"""
get current rank in all workers and pservers
"""
if not self._role_is_generated:
self.generate_role()
return self._rank
def _get_size(self):
"""
get total num of all workers and pservers
"""
if not self._role_is_generated:
self.generate_role()
return self._size
def __get_default_iface(self):
"""
get default physical interface
"""
default1 = self.__get_default_iface_from_gateway()
default2 = self.__get_default_iface_from_interfaces()
return default2 if default1 == "lo" else default1
def __get_default_iface_from_gateway(self):
"""
get default physical interface
"""
import netifaces
gateways = netifaces.gateways()
if gateways.get(netifaces.AF_INET) != None:
gateway = gateways[netifaces.AF_INET]
if len(gateway) > 0 and len(gateway[0]) > 1:
return gateway[0][1]
return "lo"
def __get_default_iface_from_interfaces(self):
"""
get default physical interface
"""
import netifaces
for intf_name in netifaces.interfaces():
addresses = netifaces.ifaddresses(intf_name)
if netifaces.AF_INET in addresses:
ipv4_addresses = addresses[netifaces.AF_INET]
for ipv4_address in ipv4_addresses:
if 'broadcast' in ipv4_address:
return intf_name
return "lo"
class UserDefinedRoleMaker(RoleMakerBase):
"""
UserDefinedRoleMaker is designed for worker and server assignment
under manual. Typically, a worker and a server node will be appointed
on each physical node, It can be assign by user.
"""
def __init__(self,
current_id=0,
role=Role.WORKER,
worker_num=0,
server_endpoints=None):
"""
UserDefinedRoleMaker is designed for worker and server assignment
under manual. Typically, a worker and a server node will be appointed
on each physical node, It can be assign by user.
"""
super(UserDefinedRoleMaker, self).__init__()
if not isinstance(server_endpoints, list):
......@@ -495,11 +950,12 @@ class UserDefinedRoleMaker(RoleMakerBase):
class UserDefinedCollectiveRoleMaker(RoleMakerBase):
"""
UserDefinedCollectiveRoleMaker is designed for worker assignment
under manual for collective mode.
"""
def __init__(self, current_id=0, worker_endpoints=None):
"""
UserDefinedCollectiveRoleMaker is designed for worker assignment
under manual for collective mode.
"""
super(UserDefinedCollectiveRoleMaker, self).__init__()
if not isinstance(worker_endpoints, list):
......
python/paddle/fluid/incubate/fleet/parameter_server/pslib/__init__.py
浏览文件 @ 371f377b
......@@ -40,7 +40,9 @@ class PSLib(Fleet):
self._client2client_max_retry = 3
def init(self, role_maker=None):
super(PSLib, self).init(MPISymetricRoleMaker())
if role_maker is None:
role_maker = MPISymetricRoleMaker()
super(PSLib, self).init(role_maker)
self._fleet_ptr = fluid.core.Fleet()
def _set_client_communication_config(self, request_timeout_ms,
......@@ -75,9 +77,10 @@ class PSLib(Fleet):
# barrier_all for init_server, wait for server starts
self._role_maker._barrier_all()
self.all_ips_ = self._role_maker._all_gather(self._local_ip)
# worker_index * 2 is for compatible with older versions of pslib
self._fleet_ptr.init_worker(self._dist_desc_str, self.all_ips_,
self._role_maker._get_size(),
self._role_maker._get_rank())
self._role_maker.worker_index() * 2)
# barrier_all for init_worker
self._role_maker._barrier_all()
# prepare for client to client communication
......@@ -160,9 +163,16 @@ class PSLib(Fleet):
else:
raise Exception(
"You should run DistributedOptimizer.minimize() first")
# server_index * 2 is for compatible with older versions of pslib
self._fleet_ptr.init_server(self._dist_desc_str,
self._role_maker._get_rank())
self._local_ip = self._fleet_ptr.run_server()
self._role_maker.server_index() * 2)
if isinstance(self._role_maker, MPISymetricRoleMaker):
self._local_ip = self._fleet_ptr.run_server()
else:
local_endpoint = self._role_maker.get_local_endpoint()
local_endpoint = local_endpoint.split(":")
self._local_ip = self._fleet_ptr.run_server(
str(local_endpoint[0]), int(local_endpoint[1]))
# barrier_all for init_server
self._role_maker._barrier_all()
......@@ -632,8 +642,8 @@ class DownpourOptimizer(DistributedOptimizer):
parameter_list,
no_grad_set,
self._strategy)
opt_info["mpi_rank"] = fleet._role_maker._get_rank()
opt_info["mpi_size"] = fleet._role_maker._get_size()
opt_info["mpi_rank"] = fleet.worker_index()
opt_info["mpi_size"] = fleet.worker_num()
fleet._set_opt_info(opt_info)
programs = [loss.block.program for loss in losses]
......
python/paddle/fluid/incubate/fleet/utils/fleet_util.py
浏览文件 @ 371f377b
......@@ -206,7 +206,7 @@ class FleetUtil(object):
pos = pos.reshape(-1)
global_pos = np.copy(pos) * 0
# mpi allreduce
fleet._role_maker._node_type_comm.Allreduce(pos, global_pos)
fleet._role_maker._all_reduce(pos, global_pos)
# reshape to its original shape
global_pos = global_pos.reshape(old_pos_shape)
......@@ -215,7 +215,7 @@ class FleetUtil(object):
old_neg_shape = np.array(neg.shape)
neg = neg.reshape(-1)
global_neg = np.copy(neg) * 0
fleet._role_maker._node_type_comm.Allreduce(neg, global_neg)
fleet._role_maker._all_reduce(neg, global_neg)
global_neg = global_neg.reshape(old_neg_shape)
# calculate auc
......@@ -1350,7 +1350,7 @@ class FleetUtil(object):
pos = pos.reshape(-1)
global_pos = np.copy(pos) * 0
# mpi allreduce
fleet._role_maker._node_type_comm.Allreduce(pos, global_pos)
fleet._role_maker._all_reduce(pos, global_pos)
# reshape to its original shape
global_pos = global_pos.reshape(old_pos_shape)
# auc neg bucket
......@@ -1358,7 +1358,7 @@ class FleetUtil(object):
old_neg_shape = np.array(neg.shape)
neg = neg.reshape(-1)
global_neg = np.copy(neg) * 0
fleet._role_maker._node_type_comm.Allreduce(neg, global_neg)
fleet._role_maker._all_reduce(neg, global_neg)
global_neg = global_neg.reshape(old_neg_shape)
num_bucket = len(global_pos[0])
......@@ -1368,7 +1368,7 @@ class FleetUtil(object):
old_metric_shape = np.array(metric.shape)
metric = metric.reshape(-1)
global_metric = np.copy(metric) * 0
fleet._role_maker._node_type_comm.Allreduce(metric, global_metric)
fleet._role_maker._all_reduce(metric, global_metric)
global_metric = global_metric.reshape(old_metric_shape)
return global_metric[0]
......
python/paddle/fluid/tests/unittests/test_dataset.py
浏览文件 @ 371f377b
......@@ -733,7 +733,7 @@ class TestDataset2(unittest.TestCase):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
try:
fleet.init(exe)
fleet.init()
except ImportError as e:
print("warning: no mpi4py")
adam = fluid.optimizer.Adam(learning_rate=0.000005)
......@@ -795,7 +795,7 @@ class TestDataset2(unittest.TestCase):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
try:
fleet.init(exe)
fleet.init()
except ImportError as e:
print("warning: no mpi4py")
adam = fluid.optimizer.Adam(learning_rate=0.000005)
......@@ -824,6 +824,10 @@ class TestDataset2(unittest.TestCase):
dataset.set_pipe_command("cat")
dataset.set_use_var(slots_vars)
dataset.load_into_memory()
try:
dataset.global_shuffle(fleet)
except:
print("warning: catch expected error")
fleet._opt_info = None
fleet._fleet_ptr = None
......
python/paddle/fluid/tests/unittests/test_fleet_rolemaker.py
浏览文件 @ 371f377b
......@@ -11,36 +11,41 @@
# 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.
"""Test cloud role maker."""
from __future__ import print_function
import os
import unittest
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
class TestCloudRoleMaker(unittest.TestCase):
"""
Test cases for PaddleCloudRoleMaker.
"""
def setUp(self):
"""Set up, set envs."""
os.environ["PADDLE_TRAINERS_NUM"] = "2"
os.environ[
"PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001,127.0.0.2:36001"
def test_tr_rolemaker(self):
"""Test tr rolenamer."""
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_TRAINER_ID"] = "0"
ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
ro.generate_role()
self.assertTrue(ro.is_worker())
self.assertFalse(ro.is_server())
self.assertEqual(ro.worker_num(), 2)
def test_ps_rolemaker(self):
"""Test ps rolemaker."""
os.environ["TRAINING_ROLE"] = "PSERVER"
os.environ["POD_IP"] = "127.0.0.1"
os.environ["PADDLE_PORT"] = "36001"
ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
ro.generate_role()
self.assertFalse(ro.is_worker())
......@@ -48,10 +53,75 @@ class TestCloudRoleMaker(unittest.TestCase):
self.assertEqual(ro.worker_num(), 2)
def test_traing_role(self):
"""Test training role."""
os.environ["TRAINING_ROLE"] = "TEST"
ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
self.assertRaises(ValueError, ro.generate_role)
def test_pslib_1(self):
"""Test cases for pslib."""
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
from paddle.fluid.incubate.fleet.parameter_server.pslib import PSLib
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
try:
import netifaces
except:
print("warning: no netifaces, skip test_pslib_1")
return
os.environ["POD_IP"] = "127.0.0.1"
os.environ["PADDLE_PORT"] = "36001"
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
os.environ["PADDLE_TRAINER_ID"] = "0"
role_maker = GeneralRoleMaker()
role_maker.generate_role()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
fleet.init(role_maker)
train_program = fluid.Program()
startup_program = fluid.Program()
scope = fluid.Scope()
with fluid.program_guard(train_program, startup_program):
show = fluid.layers.data(name="show", shape=[-1, 1], \
dtype="float32", lod_level=1, append_batch_size=False)
fc = fluid.layers.fc(input=show, size=1, act=None)
label = fluid.layers.data(name="click", shape=[-1, 1], \
dtype="int64", lod_level=1, append_batch_size=False)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(adam)
adam.minimize([cost], [scope])
fleet.run_server()
except:
print("do not support pslib test, skip")
return
from paddle.fluid.incubate.fleet.base.role_maker import \
MPISymetricRoleMaker
try:
role = MPISymetricRoleMaker()
role._all_reduce([1], [2])
except:
print("catch expected error of not inited")
try:
role = MPISymetricRoleMaker()
role._all_reduce([1], [2], "min")
except:
print("catch expected error of not inited")
try:
role = MPISymetricRoleMaker()
role._all_reduce([1], [2], "max")
except:
print("catch expected error of not inited")
try:
role = MPISymetricRoleMaker()
role._all_reduce([1], [2], "unknown")
except:
print("catch expected error of unknown type")
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_fleet_rolemaker_2.py 0 → 100644
浏览文件 @ 371f377b
# Copyright (c) 2019 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.
"""Test cases for role makers."""
from __future__ import print_function
import os
import unittest
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
class TestCloudRoleMaker2(unittest.TestCase):
"""
Test cases for paddle cloud role makers.
"""
def setUp(self):
"""Set up, set envs."""
pass
def test_pslib_2(self):
"""Test cases for pslib."""
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
from paddle.fluid.incubate.fleet.parameter_server.pslib import PSLib
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
from paddle.fluid.incubate.fleet.base.role_maker import RoleMakerBase
try:
import netifaces
except:
print("warning: no netifaces, skip test_pslib_2")
return
os.environ["POD_IP"] = "127.0.0.1"
os.environ["PADDLE_PORT"] = "36001"
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
os.environ["PADDLE_TRAINER_ID"] = "0"
os.environ["PADDLE_TRAINERS_NUM"] = "1"
place = fluid.CPUPlace()
exe = fluid.Executor(place)
try:
fleet.init(None)
except:
print("no mpi4py, skip test_pslib_2")
return
train_program = fluid.Program()
startup_program = fluid.Program()
scope = fluid.Scope()
with fluid.program_guard(train_program, startup_program):
show = fluid.layers.data(name="show", shape=[-1, 1], \
dtype="float32", lod_level=1, append_batch_size=False)
fc = fluid.layers.fc(input=show, size=1, act=None)
label = fluid.layers.data(name="click", shape=[-1, 1], \
dtype="int64", lod_level=1, append_batch_size=False)
label_cast = fluid.layers.cast(label, dtype='float32')
cost = fluid.layers.log_loss(fc, label_cast)
try:
adam = fluid.optimizer.Adam(learning_rate=0.000005)
adam = fleet.distributed_optimizer(adam)
adam.minimize([cost], [scope])
fleet.run_server()
except:
print("do not support pslib test, skip")
return
os.environ["TRAINING_ROLE"] = "wrong"
try:
role1 = GeneralRoleMaker(path="./test_gloo_1")
role1.generate_role()
except:
print("catch expected error of wrong TRAINING_ROLE")
os.environ["TRAINING_ROLE"] = "PSERVER"
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
role2 = GeneralRoleMaker(path="./test_gloo_2")
role2._finalize()
role2._all_gather(1)
role2._all_gather(1)
role2._barrier_server()
role2.all_gather(1)
role3 = GeneralRoleMaker(path="./test_gloo_3")
role3._worker_gather(1)
role3._worker_gather(1)
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
role4 = GeneralRoleMaker(path="./test_gloo_4")
role4._worker_gather(1)
role4._get_rank()
role4._get_size()
role4._all_comm.init(0, 0, "", "", "", "", "")
role5 = GeneralRoleMaker(path="./test_gloo_5")
role5.get_local_endpoint()
role5.get_local_endpoint()
role6 = GeneralRoleMaker(path="./test_gloo_6")
role6.get_trainer_endpoints()
role6.get_trainer_endpoints()
role7 = GeneralRoleMaker(path="./test_gloo_7")
role7.get_pserver_endpoints()
role7.get_pserver_endpoints()
role8 = GeneralRoleMaker(path="./test_gloo_8")
role8.is_worker()
role8.is_worker()
role9 = GeneralRoleMaker(path="./test_gloo_9")
role9.is_server()
role9.is_server()
role10 = GeneralRoleMaker(path="./test_gloo_10")
role10.is_first_worker()
role10.is_first_worker()
role11 = GeneralRoleMaker(path="./test_gloo_11")
role11.worker_index()
role11.worker_index()
role12 = GeneralRoleMaker(path="./test_gloo_12")
role12.server_index()
role12.server_index()
role13 = GeneralRoleMaker(path="./test_gloo_13")
role13.worker_num()
role13.worker_num()
role14 = GeneralRoleMaker(path="./test_gloo_14")
role14.server_num()
role14.server_num()
role15 = GeneralRoleMaker(path="./test_gloo_15")
role15._barrier_worker()
role15._barrier_worker()
role16 = GeneralRoleMaker(path="./test_gloo_16")
role16._barrier_all()
role16._barrier_all()
role17 = GeneralRoleMaker(path="./test_gloo_17")
role17._barrier_server()
role17._barrier_server()
role18 = GeneralRoleMaker(path="./test_gloo_18")
role18._worker_num()
role18._worker_num()
role19 = GeneralRoleMaker(path="./test_gloo_19")
role19._server_num()
role19._server_num()
role20 = GeneralRoleMaker(path="./test_gloo_20")
a = [1]
b = [0]
role20._all_reduce(a, b)
role21 = GeneralRoleMaker(path="./test_gloo_21")
role21.all_reduce_worker([], [])
role21.all_reduce_worker([], [])
role21.barrier_worker()
role21.barrier_all()
role22 = GeneralRoleMaker(path="./test_gloo_22")
role22._get_rank()
role22._get_rank()
os.environ["PADDLE_PSERVER_ID"] = "0"
role23 = GeneralRoleMaker(path="./test_gloo_23")
role23._get_size()
role23._get_size()
with open("test_fleet_gloo_role_maker_1.txt", "w") as f:
data = "1 1 1 1\n"
f.write(data)
dataset = fluid.DatasetFactory().create_dataset("InMemoryDataset")
dataset.set_filelist(["test_fleet_gloo_role_maker_1.txt"])
dataset.set_use_var([show, label])
dataset.load_into_memory()
dataset.get_memory_data_size(fleet)
dataset.get_shuffle_data_size(fleet)
os.remove("./test_fleet_gloo_role_maker_1.txt")
class TmpClass():
"""
dummy tmp class
"""
def __init__(self):
pass
def all_reduce_worker(self, input, output):
"""
dummy all reduce worker
Args:
input(None): fake input
output(None): fale output
"""
pass
def barrier_worker(self):
"""
dummy barrier worker
"""
pass
from paddle.fluid.incubate.fleet.base.fleet_base import Fleet
class TmpFleet(Fleet):
"""
dummy tmp fleet
"""
def __init__(self):
super(Fleet, self).__init__()
self._role_maker = None
def init_worker(self):
"""
dummy init worker
"""
pass
def init_server(self, model_dir=None):
"""
dummy init server
Args:
model_dir(None): fake model_dir
"""
pass
def run_server(self):
"""
dummy run server
"""
pass
def stop_worker(self):
"""
dummy stop worker
"""
pass
def distributed_optimizer(self, optimizer, strategy=None):
"""
dummy distributed optimizer
Args:
optimizer(None): fake optimizer
strategy(None): fake strategy
"""
pass
def save_inference_model(self):
"""
dummy save inference model
"""
pass
def save_persistables(self):
"""
dummy save persistables
"""
pass
os.environ["TRAINING_ROLE"] = "TRAINER"
tmp = TmpFleet()
tmp._role_maker = TmpClass()
tmp.all_reduce_worker([], [])
tmp.barrier_worker()
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
tmp = RoleMakerBase()
tmp.all_gather(1)
tmp.all_reduce_worker([], [])
tmp.barrier_worker()
tmp.barrier_all()
from paddle.fluid.incubate.fleet.base.role_maker import \
MPISymetricRoleMaker
tmp1 = MPISymetricRoleMaker()
tmp1.all_gather(1)
tmp1.all_gather(1)
tmp2 = MPISymetricRoleMaker()
tmp2.all_reduce_worker([], [])
tmp3 = MPISymetricRoleMaker()
tmp3.barrier_worker()
tmp3.barrier_worker()
tmp4 = MPISymetricRoleMaker()
tmp4.barrier_all()
tmp4.barrier_all()
if __name__ == "__main__":
unittest.main()
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