class DistributedTranspiler(Fleet):
"""
A subclass for compatibility with fluid.transpiler.DistributeTranspiler.
"""
def __init__(self):
super(DistributedTranspiler, self).__init__(Mode.TRANSPILER)
self._transpile_config = None
self._transpiler = None
self._origin_program = None
self.startup_program = None
self.main_program = None
self._communicator = None
def init_worker(self):
"""
`init_worker` has many many functions to do before training,
first, wait for all parameter servers launch completely.
second, run executor to initialize startup program
third, wait for all worker initialize completely.
Returns:
None
"""
# if MPISymetricRoleMaker is defined
# we suppose a user wants to submit job on mpi cluster
if isinstance(self._role_maker, MPISymetricRoleMaker):
# check whether server has been initialized
from paddle.fluid.transpiler.details.checkport import wait_server_ready
wait_server_ready(fleet.server_endpoints(to_string=False))
if not self._transpile_config.sync_mode:
self._communicator = Communicator(self.main_program)
if not self._communicator.is_running():
self._communicator.start()
else:
warnings.warn("communicator has been initialized, skip")
def init_server(self, model_dir=None):
"""
`init_server` has many many functions to do before start pserver,
first, run executor to initialize startup program,
second, if the `model_dir` is not empty, it will load parameters from it for increment training.
Args:
model_dir(str): The directory path.
Returns:
None
"""
if not self.startup_program:
raise ValueError(
"startup_program is None, need invoke DistributedOptimizer.minimize first"
)
self._executor.run(self.startup_program)
if model_dir:
if not os.path.isdir(model_dir):
raise ValueError("There is no directory named '%s'", model_dir)
io.load_persistables(self._executor, model_dir, self.main_program)
def run_server(self):
"""
`run_server` execute executor to start pserver main program.
Returns:
None
"""
if not self.main_program:
raise ValueError(
"main_program is None, need invoke DistributedOptimizer.minimize first"
)
self._executor.run(self.main_program)
def stop_worker(self):
"""
Close this executor.
For the distributed training, this method would free the resource on PServers related to
the current Trainer.
Returns:
None
"""
if not self._transpile_config.sync_mode and self._communicator.is_running(
):
self._communicator.stop()
self._executor.close()
if isinstance(self._role_maker, MPISymetricRoleMaker):
self._role_maker._finalize()
def distributed_optimizer(self, optimizer, strategy=None):
"""
Optimizer for distributed training.
For the distributed training, this method would rebuild a new instance of DistributedOptimizer.
Which has basic Optimizer function and special features for distributed training.
Args:
optimizer(Optimizer): The executor to run for init server.
strategy(DistributeTranspilerConfig): Extra properties for distributed optimizer.
Returns:
TranspilerOptimizer: subclass of DistributedOptimizer.
"""
if not isinstance(optimizer, Optimizer):
raise ValueError("optimizer must be an instance of Optimizer")
self._optimizer = TranspilerOptimizer(optimizer, strategy)
return self._optimizer
def save_inference_model(self,
executor,
dirname,
feeded_var_names,
target_vars,
main_program=None,
export_for_deployment=True):
"""
Prune the given `main_program` to build a new program especially for inference,
and then save it and all related parameters to given `dirname` by the `executor`.
"""
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save_inference_model() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save_inference_model() function, executor must be as Executor type"
)
if main_program is not None:
if isinstance(main_program, CompiledProgram):
raise TypeError(
"in fleet.save_inference_model() function, main_program must be as Program type, CompiledProgram is not allowed"
)
io.save_inference_model(dirname, feeded_var_names, target_vars,
executor, main_program, None, None,
export_for_deployment)
else:
io.save_inference_model(dirname, feeded_var_names, target_vars,
executor, self._origin_program, None, None,
export_for_deployment, True)
model_basename = "__model__"
model_filename = os.path.join(dirname, model_basename)
with open(model_filename, "rb") as f:
program_desc_str = f.read()
program = Program.parse_from_string(program_desc_str)
program._copy_dist_param_info_from(self.main_program)
self.save_persistables(executor, dirname, program)
def save_persistables(self, executor, dirname, main_program=None):
"""
This function filters out all variables with `persistable==True` from the
give `main_program` and then saves these variables to the folder `dirname`
or file `filename`.
The `dirname` is used to specify the folder where persistable variables
are going to be saved. If you would like to save variables in separate
files, set `filename` None; if you would like to save all variables in a
single file, use `filename` to specify the file name.
"""
if isinstance(executor, ParallelExecutor):
raise TypeError(
"in fleet.save_persistables() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if not isinstance(executor, Executor):
raise TypeError(
"in fleet.save_persistables() function, executor must be as Executor type"
)
if main_program is None:
main_program = self.main_program
if isinstance(main_program, CompiledProgram):
raise TypeError(
"in fleet.save_persistables() function, main_program must be as Program type, CompiledProgram is not allowed"
)
if not main_program._is_distributed:
raise ValueError(
"main_program is for local, may not use fleet.save_persistables"
)
io.save_persistables(executor, dirname, main_program, None)
def _transpile(self, config):
if not isinstance(config, DistributeTranspilerConfig):
raise TypeError(
"config must be an instance of DistributeTranspilerConfig")
if not config.sync_mode:
config.runtime_split_send_recv = True
# _origin_program is a deep copy for default_main_program, for inference
self._origin_program = default_main_program().clone(for_test=False)
self._transpile_config = config
self._transpiler = OriginTranspiler(config)
if self.is_worker():
self._transpiler.transpile(
trainer_id=fleet.worker_index(),
pservers=fleet.server_endpoints(to_string=True),
trainers=fleet.worker_num(),
sync_mode=config.sync_mode)
if isinstance(self._role_maker, MPISymetricRoleMaker):
config.wait_port = False
self.main_program = self._transpiler.get_trainer_program(
wait_port=config.wait_port)
self.startup_program = default_startup_program()
else:
self._transpiler.transpile(
trainer_id=fleet.worker_index(),
pservers=fleet.server_endpoints(to_string=True),
trainers=fleet.worker_num(),
sync_mode=config.sync_mode,
current_endpoint=self.server_endpoints()[self.server_index()])
self.main_program, self.startup_program = \
self._transpiler.get_pserver_programs(self.server_endpoints()[self.server_index()])
class DistributedTranspiler(Fleet):
"""
A subclass for compatibility with fluid.transpiler.DistributeTranspiler.
"""
def __init__(self):
super(DistributedTranspiler, self).__init__(Mode.TRANSPILER)
self._transpile_config = None
self._transpiler = None
self.startup_program = None
self.main_program = None
self._communicator = None
def init_worker(self):
"""
`init_worker` has many many functions to do before training,
first, wait for all parameter servers launch completely.
second, run executor to initialize startup program
third, wait for all worker initialize completely.
Returns:
None
"""
if not self._transpile_config.sync_mode:
self._communicator = Communicator(self.main_program)
self._communicator.start()
def init_server(self, model_dir=None):
"""
`init_server` has many many functions to do before start pserver,
first, run executor to initialize startup program,
second, if the `model_dir` is not empty, it will load parameters from it for increment training.
Args:
model_dir(str): The directory path.
Returns:
None
"""
if not self.startup_program:
raise ValueError(
"startup_program is None, need invoke DistributedOptimizer.minimize first"
)
self._executor.run(self.startup_program)
if model_dir:
if not os.path.isdir(model_dir):
raise ValueError("There is no directory named '%s'", model_dir)
io.load_persistables(self._executor, model_dir,
self.startup_program)
def run_server(self):
"""
`run_server` execute executor to start pserver main program.
Returns:
None
"""
if not self.main_program:
raise ValueError(
"main_program is None, need invoke DistributedOptimizer.minimize first"
)
self._executor.run(self.main_program)
def stop_worker(self):
"""
Close this executor.
For the distributed training, this method would free the resource on PServers related to
the current Trainer.
Returns:
None
"""
if not self._transpile_config.sync_mode:
self._communicator.stop()
self._executor.close()
def distributed_optimizer(self, optimizer, strategy=None):
"""
Optimizer for distributed training.
For the distributed training, this method would rebuild a new instance of DistributedOptimizer.
Which has basic Optimizer function and special features for distributed training.
Args:
optimizer(Optimizer): The executor to run for init server.
strategy(dict): Extra properties for distributed optimizer.
Returns:
TranspilerOptimizer: subclass of DistributedOptimizer.
"""
if not isinstance(optimizer, Optimizer):
raise ValueError("optimizer must be an instance of Optimizer")
self._optimizer = TranspilerOptimizer(optimizer, strategy)
return self._optimizer
def save_inference_model(self,
executor,
dirname,
feeded_var_names,
target_vars,
main_program=None,
export_for_deployment=True):
"""
Prune the given `main_program` to build a new program especially for inference,
and then save it and all related parameters to given `dirname` by the `executor`.
"""
io.save_inference_model(dirname, feeded_var_names, target_vars,
executor, main_program, None, None,
export_for_deployment)
def save_persistables(self, executor, dirname, main_program=None):
"""
This function filters out all variables with `persistable==True` from the
give `main_program` and then saves these variables to the folder `dirname`
or file `filename`.
The `dirname` is used to specify the folder where persistable variables
are going to be saved. If you would like to save variables in separate
files, set `filename` None; if you would like to save all variables in a
single file, use `filename` to specify the file name.
"""
io.save_persistables(executor, dirname, main_program, None)
def _transpile(self, config):
if not isinstance(config, DistributeTranspilerConfig):
raise ValueError(
"config must be an instance of DistributeTranspilerConfig")
if not config.sync_mode:
config.runtime_split_send_recv = True
self._transpile_config = config
self._transpiler = OriginTranspiler(config)
self._transpiler.transpile(
trainer_id=fleet.worker_index(),
pservers=fleet.server_endpoints(to_string=True),
trainers=fleet.worker_num(),
sync_mode=config.sync_mode)
if self.is_worker():
self.main_program = self._transpiler.get_trainer_program()
self.startup_program = default_startup_program()
else:
self.main_program, self.startup_program = \
self._transpiler.get_pserver_programs(self.server_endpoints()[self.server_index()])