DistributeTranspiler¶

class paddle.fluid.transpiler.distribute_transpiler. DistributeTranspiler ( config=None ) [source]

api_attr

Static Graph

DistributeTranspiler

Convert the fluid program to distributed data-parallelism programs. Supports two modes: parameter server(pserver) mode and nccl2 mode.

In pserver mode, the main_program will be transformed to use a remote parameter server to do parameter optimization. And the optimization graph will be put into a parameter server program.

In nccl2 mode, the transpiler will append a NCCL_ID broadcasting op in startup_program to share the NCCL_ID across the job nodes. After transpile_nccl2 called, you *must* pass trainer_id and num_trainers argument to ParallelExecutor to enable NCCL2 distributed mode.

Examples

           x = fluid.data(name='x', shape=[13], dtype='float32')
y = fluid.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)

cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(cost)

sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_loss)

# for pserver mode
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4
role = "PSERVER"
t = fluid.DistributeTranspiler()
t.transpile(
     trainer_id, pservers=pserver_endpoints, trainers=trainers)
if role == "PSERVER":
     pserver_program = t.get_pserver_program(current_endpoint)
     pserver_startup_program = t.get_startup_program(current_endpoint,
                                                    pserver_program)
elif role == "TRAINER":
     trainer_program = t.get_trainer_program()

# for nccl2 mode
trainer_num = 2
trainer_id = 0
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id=trainer_id, trainers=trainer_endpoints, current_endpoint="192.168.0.1:6174")
exe = fluid.ParallelExecutor(
    use_cuda=True,
    loss_name=avg_loss.name,
    num_trainers=trainer_num,
    trainer_id=trainer_id
)

          

transpile ( trainer_id, program=None, pservers='127.0.0.1:6174', trainers=1, sync_mode=True, startup_program=None, current_endpoint='127.0.0.1:6174' ) transpile¶

Transpile the input program to distributed programs with config and arguments.

Parameters

trainer_id (int) – id for current trainer worker, if you have n workers, the id may range from 0 ~ n-1
program (Program|None) – program to transpile, default is fluid.default_main_program().
startup_program (Program|None) – startup_program to transpile, default is fluid.default_startup_program().
pservers (str) – comma separated ip:port string for the pserver list.
trainers (int|str) – in pserver mode this is the number of trainers, in nccl2 mode this is a string of trainer endpoints.
sync_mode (bool) – Do sync training or not, default is True.
startup_program – startup_program to transpile, default is fluid.default_main_program().
current_endpoint (str) – need pass current endpoint when transpile as nccl2 distributed mode. In pserver mode this argument is not used.

Examples

             transpiler = fluid.DistributeTranspiler()
t.transpile(
    trainer_id=0,
    pservers="127.0.0.1:7000,127.0.0.1:7001",
    trainers=2,
    sync_mode=False,
    current_endpoint="127.0.0.1:7000")

            

get_trainer_program ( wait_port=True ) get_trainer_program¶

Get transpiled trainer side program. The program on trainer side compared with origin program has following difference:

Delete optimizer related op, because parameter updated on Pserver

After the op which computed gradient of each parameter, add Send_op and Recv_op

Parameters

wait_port (bool) – Whether to wait for the parameter server to be ready before returning to program,
True (default is) –

Returns

trainer side program.

Return type

Program

Examples

             import paddle.fluid as fluid
#this is an example, find available endpoints in your case
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_id = 0
trainers = 4
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, trainers=trainers, pservers=pserver_endpoints)
trainer_program = t.get_trainer_program()

            

get_pserver_program ( endpoint ) get_pserver_program¶

Get parameter server side program.The program on pserver side compared with origin program has following difference:

Only the following op is included: optimize-related op and communication-related op

NO.0 block only has variable definitions and listen_and_serv_op

Every variable which need to be updated has a unique block

Parameters: endpoint (str) – current parameter server endpoint.
Returns: the program for current parameter server to run.
Return type: Program

Examples

             import paddle.fluid as fluid
#this is an example, find available endpoints in your case
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4
t = fluid.DistributeTranspiler()
t.transpile(
     trainer_id, pservers=pserver_endpoints, trainers=trainers)
pserver_program = t.get_pserver_program(current_endpoint)

            

get_pserver_programs ( endpoint ) get_pserver_programs¶

Get pserver side main program and startup program for distributed training. The main_program returned by this function is consistent with the return value of the function get_pserver_program .

Parameters: endpoint (str) – current pserver endpoint.
Returns: (main_program, startup_program), of type “Program”
Return type: tuple

Examples

             import paddle.fluid as fluid
#this is an example, find available endpoints in your case
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4
t = fluid.DistributeTranspiler()
t.transpile(
     trainer_id, pservers=pserver_endpoints, trainers=trainers)
pserver_program, pserver_startup_program = t.get_pserver_programs(current_endpoint)

            

get_startup_program ( endpoint, pserver_program=None, startup_program=None ) get_startup_program¶

Deprecated

Get startup program for current parameter server. Modify operator input variables if there are variables that were split to several blocks.

Parameters

endpoint (str) – current pserver endpoint.
pserver_program (Program) – deprecated, call get_pserver_program first.
startup_program (Program) – deprecated, should pass startup_program when initializing

Returns

parameter server side startup program.

Return type

Program

Examples

             pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4

t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
                                                pserver_program)

            