使用FleetAPI进行分布式训练
FleetAPI 设计说明
Fleet是PaddlePaddle分布式训练的高级API。Fleet的命名出自于PaddlePaddle,象征一个舰队中的多只双桨船协同工作。Fleet的设计在易用性和算法可扩展性方面做出了权衡。用户可以很容易从单机版的训练程序,通过添加几行代码切换到分布式训练程序。此外,分布式训练的算法也可以通过Fleet API接口灵活定义。具体的设计原理可以参考Fleet API设计文档。当前FleetAPI还处于paddle.fluid.incubate目录下,未来功能完备后会放到paddle.fluid目录中,欢迎持续关注。
Fleet API快速上手示例
下面会针对Fleet API最常见的两种使用场景,用一个模型做示例,目的是让用户有快速上手体验的模板。快速上手的示例源代码可以在Fleet Quick Start 找到。
- 假设我们定义MLP网络如下:
- import paddle.fluid as fluid
- def mlp(input_x, input_y, hid_dim=128, label_dim=2):
- fc_1 = fluid.layers.fc(input=input_x, size=hid_dim, act='tanh')
- fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim, act='tanh')
- prediction = fluid.layers.fc(input=[fc_2], size=label_dim, act='softmax')
- cost = fluid.layers.cross_entropy(input=prediction, label=input_y)
- avg_cost = fluid.layers.mean(x=cost)
- return avg_cost
- 定义一个在内存生成数据的Reader如下:
- import numpy as np
- def gen_data():
- return {"x": np.random.random(size=(128, 32)).astype('float32'),
- "y": np.random.randint(2, size=(128, 1)).astype('int64')}
- 单机Trainer定义
- import paddle.fluid as fluid
- from nets import mlp
- from utils import gen_data
- input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
- input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
- cost = mlp(input_x, input_y)
- optimizer = fluid.optimizer.SGD(learning_rate=0.01)
- optimizer.minimize(cost)
- place = fluid.CUDAPlace(0)
- exe = fluid.Executor(place)
- exe.run(fluid.default_startup_program())
- step = 1001
- for i in range(step):
- cost_val = exe.run(feed=gen_data(), fetch_list=[cost.name])
- print("step%d cost=%f" % (i, cost_val[0]))
- Parameter Server训练方法
参数服务器方法对于大规模数据,简单模型的并行训练非常适用,我们基于单机模型的定义给出使用Parameter Server进行训练的示例如下:
- import paddle.fluid as fluid
- from nets import mlp
- from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
- from paddle.fluid.incubate.fleet.base import role_maker
- from utils import gen_data
- input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
- input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
- cost = mlp(input_x, input_y)
- optimizer = fluid.optimizer.SGD(learning_rate=0.01)
- role = role_maker.PaddleCloudRoleMaker()
- fleet.init(role)
- optimizer = fleet.distributed_optimizer(optimizer)
- optimizer.minimize(cost)
- if fleet.is_server():
- fleet.init_server()
- fleet.run_server()
- elif fleet.is_worker():
- place = fluid.CPUPlace()
- exe = fluid.Executor(place)
- exe.run(fluid.default_startup_program())
- step = 1001
- for i in range(step):
- cost_val = exe.run(
- program=fluid.default_main_program(),
- feed=gen_data(),
- fetch_list=[cost.name])
- print("worker_index: %d, step%d cost = %f" %
- (fleet.worker_index(), i, cost_val[0]))
- Collective训练方法
Collective Training通常在GPU多机多卡训练中使用,一般在复杂模型的训练中比较常见,我们基于上面的单机模型定义给出使用Collective方法进行分布式训练的示例如下:
- import paddle.fluid as fluid
- from nets import mlp
- from paddle.fluid.incubate.fleet.collective import fleet
- from paddle.fluid.incubate.fleet.base import role_maker
- from utils import gen_data
- input_x = fluid.layers.data(name="x", shape=[32], dtype='float32')
- input_y = fluid.layers.data(name="y", shape=[1], dtype='int64')
- cost = mlp(input_x, input_y)
- optimizer = fluid.optimizer.SGD(learning_rate=0.01)
- role = role_maker.PaddleCloudRoleMaker(is_collective=True)
- fleet.init(role)
- optimizer = fleet.distributed_optimizer(optimizer)
- optimizer.minimize(cost)
- place = fluid.CUDAPlace(0)
- exe = fluid.Executor(place)
- exe.run(fluid.default_startup_program())
- step = 1001
- for i in range(step):
- cost_val = exe.run(
- program=fluid.default_main_program(),
- feed=gen_data(),
- fetch_list=[cost.name])
- print("worker_index: %d, step%d cost = %f" %
- (fleet.worker_index(), i, cost_val[0]))
更多使用示例
Fleet API相关的接口说明
Fleet API接口
- init(role_maker=None)
- fleet初始化,需要在使用fleet其他接口前先调用,用于定义多机的环境配置
- is_worker()
- Parameter Server训练中使用,判断当前节点是否是Worker节点,是则返回True,否则返回False
- is_server(model_dir=None)
- Parameter Server训练中使用,判断当前节点是否是Server节点,是则返回True,否则返回False
- init_server()
- Parameter Server训练中,fleet加载model_dir中保存的模型相关参数进行parameter server的初始化
- run_server()
- Parameter Server训练中使用,用来启动server端服务
- init_worker()
- Parameter Server训练中使用,用来启动worker端服务
- stop_worker()
- 训练结束后,停止worker
- distributed_optimizer(optimizer, strategy=None)
- 分布式优化算法装饰器,用户可带入单机optimizer,并配置分布式训练策略,返回一个分布式的optimizer
RoleMaker
MPISymetricRoleMaker
描述:MPISymetricRoleMaker会假设每个节点启动两个进程,1worker+1pserver,这种RoleMaker要求用户的集群上有mpi环境。
示例:
- from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
- from paddle.fluid.incubate.fleet.base import role_maker
- role = role_maker.MPISymetricRoleMaker()
- fleet.init(role)
- 启动方法:
- mpirun -np 2 python trainer.py
PaddleCloudRoleMaker
描述:PaddleCloudRoleMaker是一个高级封装,支持使用paddle.distributed.launch或者paddle.distributed.launch_ps启动脚本
Parameter Server训练示例:
- from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
- from paddle.fluid.incubate.fleet.base import role_maker
- role = role_maker.PaddleCloudRoleMaker()
- fleet.init(role)
- 启动方法:
- python -m paddle.distributed.launch_ps --worker_num 2 --server_num 2 trainer.py
- Collective训练示例:
- from paddle.fluid.incubate.fleet.collective import fleet
- from paddle.fluid.incubate.fleet.base import role_maker
- role = role_maker.PaddleCloudRoleMaker(is_collective=True)
- fleet.init(role)
- 启动方法:
- python -m paddle.distributed.launch trainer.py
UserDefinedRoleMaker
描述:用户自定义节点的角色信息,IP和端口信息
示例:
- from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
- from paddle.fluid.incubate.fleet.base import role_maker
- role = role_maker.UserDefinedRoleMaker(
- current_id=int(os.getenv("CURRENT_ID")),
- role=role_maker.Role.WORKER if bool(int(os.getenv("IS_WORKER")))
- else role_maker.Role.SERVER,
- worker_num=int(os.getenv("WORKER_NUM")),
- server_endpoints=pserver_endpoints)
- fleet.init(role)
Strategy
- Parameter Server Training
- Sync_mode
- Collective Training
- LocalSGD
- ReduceGrad
Fleet Mode
- Parameter Server Training
- from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
- Collective Training
- from paddle.fluid.incubate.fleet.collective import fleet