def test_communicator_init_and_start(self):
role = role_maker.UserDefinedRoleMaker(
current_id=0,
role=role_maker.Role.WORKER,
worker_num=2,
server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
fleet.init(role)
avg_cost = self.net()
optimizer = fluid.optimizer.SGD(0.01)
strategy = DistributeTranspilerConfig()
strategy.sync_mode = True
strategy.wait_port = False
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(avg_cost)
comm = Communicator(fleet.main_program)
comm.start()
time.sleep(10)
comm.stop()
def test_communicator_init_and_start(self):
prog = fluid.Program()
comm = Communicator(prog)
comm.start()
comm.stop()
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 TheOnePSRuntime(RuntimeBase):
def __init__(self):
super(TheOnePSRuntime, self).__init__()
self._communicator = None
self._server = None
self._worker = fluid.core.DistFleetWrapper()
self._server_sub_program = []
self._heter_client = None
def _set_basic_info(self, context):
self.context = context
self.role_maker = context["role_maker"]
self.origin_main_program = context["origin_main_program"]
self.origin_startup_program = context["origin_startup_program"]
self.async_strategy = self._get_distributed_strategy()
self.compiled_strategy = self.build_compiled_startegy()
def _get_distributed_strategy(self):
strategy = None
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import \
StrategyFactory
dist_strategy = self.context["valid_strategy"]
k_steps = dist_strategy.a_sync_configs["k_steps"]
if not dist_strategy.a_sync and k_steps == 0:
strategy = StrategyFactory.create_sync_strategy()
if dist_strategy.a_sync and k_steps == 0:
strategy = StrategyFactory.create_async_strategy()
if dist_strategy.a_sync and k_steps > 0:
strategy = StrategyFactory.create_geo_strategy(k_steps)
if not strategy:
raise ValueError("k_steps must be invalid value, please check")
return strategy
def build_compiled_startegy(self):
from paddle.fluid.incubate.fleet.parameter_server.ir.public import CompileTimeStrategy
compiled_config = CompileTimeStrategy(self.origin_main_program,
self.origin_main_program,
self.async_strategy,
self.role_maker)
return compiled_config
def _init_worker(self):
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import \
SyncStrategy, GeoStrategy
is_sync = self.compiled_strategy.is_sync_mode()
worker = self._get_fleet_proto(is_server=False, is_sync=is_sync)
server = self._get_fleet_proto(is_server=True, is_sync=is_sync)
def sync_strategy_envs():
kwargs = {}
kwargs[
"pserver_endpoints"] = self.role_maker._get_pserver_endpoints(
)
kwargs["trainer_id"] = self.role_maker._worker_index()
return kwargs
proto_txt = str(worker) + "\n" + str(server)
debug = bool(int(os.getenv("PSERVER_DEBUG", "0")))
if debug:
print("worker: \n{}".format(proto_txt))
endpoints = self.compiled_strategy.get_ps_endpoints()
string_hosts = []
for idx, ep in enumerate(endpoints):
host, port = ep.split(":")
pshost = fluid.core.PSHost(host, int(port), idx)
string_hosts.append(pshost.serialize_to_string())
dense_map = self.compiled_strategy.get_the_one_recv_context(
split_dense_table=self.role_maker._is_heter_parameter_server_mode)
send_ctx = self.compiled_strategy.get_the_one_send_context(
split_dense_table=self.role_maker._is_heter_parameter_server_mode,
ep_list=endpoints)
trainer_config = self.async_strategy.get_trainer_runtime_config()
debug = bool(int(os.getenv("PSERVER_DEBUG", "0")))
if debug:
print("worker: \n{}".format(proto_txt))
print("communicator send_ctx:")
for key in send_ctx:
print("{}: {}".format(key, send_ctx[key]))
for key in dense_map:
print("{}: {}".format(key, dense_map[key]))
kwargs = {}
kwargs['need_global_step'] = "0"
kwargs["trainer_id"] = self.role_maker._role_id()
kwargs["trainers"] = self.role_maker._worker_num()
if self.role_maker._is_heter_worker():
kwargs["trainer_id"] += kwargs["trainers"]
for table in server.servers[0].tables:
if table.table_class == "BarrierTable":
kwargs["barrier_table_id"] = table.id
break
if isinstance(self.async_strategy, SyncStrategy):
sync_kwargs = sync_strategy_envs()
kwargs.update(sync_kwargs)
from paddle.fluid.communicator import Communicator, HeterClient
self._communicator = Communicator(
trainer_config.mode, kwargs,
trainer_config.get_communicator_flags())
self._communicator.init_with_ctx(send_ctx, dense_map, proto_txt,
string_hosts, fluid.global_scope())
dist_strategy = self.context["valid_strategy"]
is_test = bool(int(os.getenv("TEST_MODE", "0")))
if self.role_maker._is_first_worker(
) and self.role_maker._is_heter_parameter_server_mode:
# for ps-heter mode load all parameters on first_worker
init_params = self.compiled_strategy.get_the_one_recv_context(
split_dense_table=True, use_origin_program=True)
else:
init_params = dense_map
if not is_test:
self._communicator.init_params(init_params)
if not self._communicator.is_running():
self._communicator.start()
else:
warnings.warn("communicator has been initialized, skip")
launch_barrier = dist_strategy.a_sync_configs["launch_barrier"]
launch_barrier_flag = int(os.getenv("FLAGS_LAUNCH_BARRIER", "1"))
if launch_barrier and launch_barrier_flag:
# 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())
self._heter_client = HeterClient(
self.role_maker._get_heter_worker_endpoints(),
self.role_maker._role_id())
def _push_sparse_param(self,
var_name,
table_id=-1,
scope=fluid.global_scope()):
self._communicator.push_sparse_param(var_name, table_id, scope)
def _get_executor(self):
executor = fluid.Executor(fluid.CPUPlace())
if self.role_maker._is_heter_parameter_server_mode:
heter_worker_device_guard = self.context[
"valid_strategy"].a_sync_configs[
"heter_worker_device_guard"].upper()
if heter_worker_device_guard not in ["GPU", "XPU", "CPU"]:
raise ValueError("Heter Worker Not Support Device {}".format(
heter_worker_device_guard))
if self.role_maker._is_heter_worker():
if heter_worker_device_guard == "GPU":
executor = Executor(
fluid.CUDAPlace(
int(os.getenv("FLAGS_selected_gpus", "0"))))
elif heter_worker_device_guard == "XPU":
executor = Executor(
fluid.XPUPlace(
int(os.getenv("FLAGS_selected_xpus", "0"))))
return executor
def _get_fleet_proto(self, is_server, is_sync):
def _build_merge_accessor(ctx):
accessor = Accessor()
accessor.accessor_class = "CommMergeAccessor"
accessor.optimizer = None
if ctx.is_sparse():
accessor.feature_dim = ctx.sections()[0]
accessor.embedding_dim = ctx.sections()[1]
else:
accessor.feature_dim = ctx.sections()[0]
accessor.embedding_dim = 1
return accessor
def _build_barrier_table(idx):
table = Table()
table.id = idx
table.type = "PS_OTHER_TABLE"
table.table_class = "BarrierTable"
table.shard_num = 256
accessor = Accessor()
accessor.accessor_class = "CommMergeAccessor"
accessor.optimizer = None
accessor.feature_dim = 0
accessor.embedding_dim = 0
table.accessor = accessor
common = CommonAccessor()
common.table_name = "barrier_table"
trainer_num = self.compiled_strategy.get_trainers()
if self.role_maker._is_heter_parameter_server_mode:
trainer_num += len(
self.role_maker._get_heter_worker_endpoints())
common.trainer_num = trainer_num
common.attrs = ""
common.dims = []
common.params = []
table.common = common
return table
def _build_tensor_table(idx, tensor_dict):
table = Table()
table.id = idx
table.type = "PS_OTHER_TABLE"
table.table_class = tensor_dict["tensor_table_class"]
table.shard_num = 256
accessor = Accessor()
accessor.accessor_class = "CommMergeAccessor"
accessor.optimizer = None
accessor.feature_dim = 0
accessor.embedding_dim = 0
table.accessor = accessor
common = CommonAccessor()
common.table_name = tensor_dict["feed_var_name"]
common.trainer_num = self.compiled_strategy.get_trainers()
common.attrs = ""
common.dims = []
common.params = []
table.common = common
tensor = Tensor()
tensor.main_program_id = tensor_dict["main_program_id"]
tensor.startup_program_id = tensor_dict["startup_program_id"]
tensor.feed_var_name = tensor_dict["feed_var_name"]
tensor.fetch_var_name = tensor_dict["fetch_var_name"]
tensor.tensor_table_class = tensor_dict["tensor_table_class"]
table.tensor = tensor
return table
def _add_tensor_table(tables):
tensor_table_dict = self.compiled_strategy.get_tensor_table_dict()
program_idx = 0
for table_name in tensor_table_dict:
if tensor_table_dict[table_name]["startup_program"] != None:
tensor_table_dict[table_name][
"startup_program_id"] = program_idx
self._server_sub_program.append(
tensor_table_dict[table_name]["startup_program"].desc)
program_idx += 1
if tensor_table_dict[table_name]["main_program"] != None:
tensor_table_dict[table_name][
"main_program_id"] = program_idx
self._server_sub_program.append(
tensor_table_dict[table_name]["main_program"].desc)
program_idx += 1
# Todo: Hard code for lr_decay table apply table id
new_table = _build_tensor_table(len(tables),
tensor_table_dict[table_name])
tables.append(new_table)
return tables
def _get_tables():
send_ctx = self.compiled_strategy.get_the_one_send_context(
use_origin_program=True,
split_dense_table=self.role_maker.
_is_heter_parameter_server_mode)
tables = []
for idx, (name, ctx) in enumerate(send_ctx.items()):
if ctx.is_tensor_table() or len(ctx.origin_varnames()) < 1:
continue
table = Table()
table.id = ctx.table_id()
common = CommonAccessor()
if ctx.is_sparse():
table.type = "PS_SPARSE_TABLE"
table.shard_num = 256
if self.compiled_strategy.is_geo_mode():
table.table_class = "SparseGeoTable"
else:
table.table_class = "CommonSparseTable"
common.table_name = self.compiled_strategy.grad_name_to_param_name[
ctx.origin_varnames()[0]]
else:
table.type = "PS_DENSE_TABLE"
table.table_class = "CommonDenseTable"
table.shard_num = 256
common.table_name = "MergedDense"
common.parse_by_optimizer(
ctx.origin_varnames()[0], ctx.is_sparse(),
ctx.sections()[1] if ctx.is_sparse() else
ctx.sections()[0], self.compiled_strategy)
if ctx.is_sparse():
common.parse_entry(common.table_name,
self.origin_main_program)
if is_sync:
common.sync = "true"
else:
common.sync = "false"
table.common = common
accessor = _build_merge_accessor(ctx)
table.accessor = accessor
tables.append(table)
tensor_table_dict = self.compiled_strategy.get_tensor_table_dict()
if len(tensor_table_dict) > 0:
tables = _add_tensor_table(tables)
else:
empty_porgram = Program()
self._server_sub_program.append(empty_porgram.desc)
barrier_table = _build_barrier_table(len(tables))
tables.append(barrier_table)
return tables
if is_server:
server = Server()
downpour_server = DownpourServer()
service = Service()
downpour_server.set_service_param(service)
tables = _get_tables()
downpour_server.tables = tables
server.add_server(downpour_server)
return server
else:
worker = Worker()
downpour_worker = DownpourWorker()
tables = _get_tables()
downpour_worker.tables = tables
worker.add_worker(downpour_worker)
return worker
def _init_server(self, dirname=None, var_names=None, **kwargs):
if self.role_maker._is_heter_worker():
self._init_heter_worker()
return
role_id = self.compiled_strategy.get_role_id()
endpoints = self.compiled_strategy.get_ps_endpoints()
is_sync = self.compiled_strategy.is_sync_mode()
trainers = self.compiled_strategy.get_trainers()
server = self._get_fleet_proto(is_server=True, is_sync=is_sync)
proto_txt = str(server)
debug = bool(os.getenv("PSERVER_DEBUG", "0"))
if debug:
print("server: \n{}".format(proto_txt))
string_hosts = []
for idx, ep in enumerate(endpoints):
host, port = ep.split(":")
pshost = fluid.core.PSHost(host, int(port), idx)
string_hosts.append(pshost.serialize_to_string())
self._server = fluid.core.DistFleetWrapper()
self._server.init_server(proto_txt, string_hosts, role_id, trainers,
self._server_sub_program)
from paddle.fluid.incubate.fleet.parameter_server.ir.public import get_sparse_tablenames
dist_varnames = get_sparse_tablenames(self.origin_main_program, True)
sparse_varnames = get_sparse_tablenames(self.origin_main_program,
False)
distributed_varnames = dist_varnames + sparse_varnames
if var_names is None:
load_varnames = distributed_varnames
else:
for var_name in var_names:
if var_name not in distributed_varnames:
raise ValueError(
"fleet.init server can only load sparse variables in {}"
.format(distributed_varnames))
load_varnames = var_names
if dirname is None or not load_varnames:
return
sparse_table_maps = {}
for table in server.servers[0].tables:
if table.type == "PS_SPARSE_TABLE" and table.common is not None:
sparse_table_maps[table.common.table_name] = table.id
dirname = os.path.normpath(dirname)
pserver_id = self.role_maker._role_id()
import time
begin = time.time()
for var_name in load_varnames:
table_id = sparse_table_maps[var_name]
path = os.path.join(dirname, var_name + PSERVER_SAVE_SUFFIX,
"{}.block{}.txt".format(var_name, pserver_id))
meta = os.path.join(dirname, var_name + PSERVER_SAVE_SUFFIX,
"{}.block{}.meta".format(var_name, pserver_id))
self._server.load_sparse(path, meta, table_id)
end = time.time()
print("init sparse variables: {} cost time: {}".format(
load_varnames, end - begin))
def _run_server(self):
if self.role_maker._is_heter_worker():
self._run_heter_worker()
return
ep = self.compiled_strategy.get_ps_endpoint()
host, port = ep.split(":")
self._server.run_server(host, int(port))
def _init_heter_worker(self):
executor = self._get_executor()
executor.run(fluid.default_startup_program())
self._init_worker()
def _run_heter_worker(self):
executor = self._get_executor()
executor.run(fluid.default_main_program())
def _stop_worker(self):
self._communicator.stop()
if self.role_maker._is_heter_parameter_server_mode and self.role_maker._is_worker(
):
self._heter_client.stop()
executor = self._get_executor()
executor.close()
@staticmethod
def __exclude_vars(exclude_var_names=[]):
def is_valid(var):
if var.name in exclude_var_names:
return False
from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
origin_varname, _, _ = _get_varname_parts(var.name)
if origin_varname.endswith("@GRAD"):
return False
if origin_varname == "learning_rate_0":
return False
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == core.VarDesc.VarType.READER:
return False
return var.persistable
return is_valid
def _save_sparse_params(self, executor, dirname, context, main_program,
mode):
from paddle.fluid.incubate.fleet.parameter_server.ir.public import get_sparse_tablenames
distributed_varnames = get_sparse_tablenames(
self.compiled_strategy.origin_main_program, True)
values = []
for id, names in context.items():
if names not in distributed_varnames:
# only save sparse param to local
self._worker.recv_and_save_model(id, dirname)
# save sparse & distributed param on server
self._worker.save_one_model(id, dirname, mode)
values.extend(names)
return values
def _save_distributed_persistables(self,
executor,
dirname,
main_program,
mode=0):
denses = self.compiled_strategy.get_the_one_recv_context(
is_dense=True,
split_dense_table=self.role_maker._is_heter_parameter_server_mode,
use_origin_program=True)
sparses = self.compiled_strategy.get_the_one_recv_context(
is_dense=False,
split_dense_table=self.role_maker._is_heter_parameter_server_mode,
use_origin_program=True)
sparse_varnames = self._save_sparse_params(executor, dirname, sparses,
main_program, mode)
recv_dense_varnames = []
for id, names in denses.items():
recv_dense_varnames.extend(names)
saved_varnames = sparse_varnames
remaining_vars = list(
filter(TheOnePSRuntime.__exclude_vars(saved_varnames),
main_program.list_vars()))
fluid.io.save_vars(executor,
main_program=main_program,
dirname=dirname,
vars=remaining_vars)
def _ps_inference_save_persistables(self,
executor,
dirname,
main_program=None,
mode=0,
**kwargs):
"""
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.compiled_strategy.get_origin_ps_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"
)
# Todo(MrChengmo): Save optimizer status
self._save_distributed_persistables(executor, dirname, main_program,
mode)
def _ps_inference_save_inference_model(self,
executor,
dirname,
feeded_var_names,
target_vars,
main_program=None,
export_for_deployment=True,
mode=0):
"""
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"
)
fluid.io.save_inference_model(dirname, feeded_var_names,
target_vars, executor, main_program,
None, None, export_for_deployment)
else:
fluid.io.save_inference_model(dirname, feeded_var_names,
target_vars, executor,
self.origin_main_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(fluid.default_main_program())
self._ps_inference_save_persistables(executor, dirname, program,
mode)
def _save_inference_model(self, *args, **kwargs):
self._ps_inference_save_inference_model(*args, **kwargs)
def _save_persistables(self, *args, **kwargs):
self._ps_inference_save_persistables(*args, **kwargs)
def _shrink(self, threshold):
import paddle.distributed.fleet as fleet
fleet.util.barrier()
if self.role_maker._is_first_worker():
sparses = self.compiled_strategy.get_the_one_recv_context(
is_dense=False,
split_dense_table=self.role_maker.
_is_heter_parameter_server_mode,
use_origin_program=True)
for id, names in sparses.items():
self._worker.shrink_sparse_table(id, threshold)
fleet.util.barrier()
class ParameterServerRuntime(RuntimeBase):
def __init__(self):
super(ParameterServerRuntime, self).__init__()
self._communicator = None
def _set_basic_info(self, context):
self.context = context
self.role_maker = context["role_maker"]
self.origin_main_program = context["origin_main_program"]
self.origin_startup_program = context["origin_startup_program"]
self.async_strategy = self._get_distributed_strategy()
self.compiled_strategy = self.build_compiled_startegy()
def _get_distributed_strategy(self):
strategy = None
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
dist_strategy = self.context["valid_strategy"]
k_steps = dist_strategy.a_sync_configs["k_steps"]
if not dist_strategy.a_sync and k_steps == 0:
strategy = StrategyFactory.create_sync_strategy()
if dist_strategy.a_sync and k_steps == 0:
strategy = StrategyFactory.create_async_strategy()
if dist_strategy.a_sync and k_steps > 0:
strategy = StrategyFactory.create_geo_strategy(k_steps)
if not strategy:
raise ValueError("k_steps must be invalid value, please check")
return strategy
def build_compiled_startegy(self):
from paddle.fluid.incubate.fleet.parameter_server.ir.public import CompileTimeStrategy
compiled_config = CompileTimeStrategy(self.origin_main_program,
self.origin_main_program,
self.async_strategy,
self.role_maker)
return compiled_config
def _load_sparse_params(self,
executor,
dirname,
varnames,
main_program=None):
assert vars != None
check_vars = []
load_prog = Program()
load_block = load_prog.global_block()
def _in_varnames(var):
return var.name in varnames
load_vars = list(
filter(_in_varnames,
fluid.default_main_program().list_vars()))
if main_program is None:
main_program = self.origin_main_program
from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
for each_var in load_vars:
assert isinstance(each_var, Variable)
origin_varname, _, _ = _get_varname_parts(each_var.name)
new_var = fluid.io._clone_var_in_block_(load_block, each_var)
var_path = os.path.join(dirname, origin_varname)
if not os.path.exists(var_path):
raise ValueError(
"SelectedRows var {} can not find at {}".format(
new_var.name, var_path))
if os.path.isfile(var_path):
load_block.append_op(type='sparse_tensor_load',
inputs={},
outputs={'Out': [new_var]},
attrs={
'file_path':
os.path.join(dirname, origin_varname),
'node_index':
self.role_maker._server_index(),
'node_num':
self.role_maker._server_num(),
'shape':
each_var.shape
})
check_vars.append(each_var)
executor.run(load_prog)
def _load_distributed_params(self, dirname, varnames):
from paddle.fluid.communicator import LargeScaleKV
from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
scale_kv = LargeScaleKV()
for varname in varnames:
origin_varname, _, _ = _get_varname_parts(varname)
sparse_dir = os.path.join(dirname, origin_varname, varname)
scale_kv.load(varname, sparse_dir)
@staticmethod
def __exclude_vars(exclude_var_names=[]):
def is_valid(var):
if var.name in exclude_var_names:
return False
from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
origin_varname, _, _ = _get_varname_parts(var.name)
if origin_varname.endswith("@GRAD"):
return False
if origin_varname == "learning_rate_0":
return False
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == core.VarDesc.VarType.READER:
return False
return var.persistable
return is_valid
def _init_worker(self):
def sync_strategy_envs():
kwargs = {}
kwargs[
"pserver_endpoints"] = self.role_maker._get_pserver_endpoints(
)
kwargs["trainer_id"] = self.role_maker._worker_index()
return kwargs
def geo_strategy_envs():
from paddle.fluid.incubate.fleet.parameter_server.ir.public import get_sparse_tablenames
def get_sparse_attrs():
opt_init_map = {}
opt_init_map["gaussian_random"] = ["seed", "mean", "std"]
opt_init_map["fill_constant"] = ["value"]
opt_init_map["uniform_random"] = ["seed", "min", "max"]
opt_init_map["truncated_gaussian_random"] = [
"seed", "mean", "std"
]
dist_varnames = get_sparse_tablenames(self.origin_main_program,
True)
sparse_varnames = get_sparse_tablenames(
self.origin_main_program, False)
if len(dist_varnames) != 0:
raise ValueError(
"GeoStrategy can not support large scale embeding now, please use fluid.layers.embedding"
)
init_attrs = []
for value_name in sparse_varnames:
value_var = self.origin_main_program.global_block(
).vars[value_name]
value_attr = [
value_name,
",".join([str(dim) for dim in value_var.shape])
]
for op in self.origin_startup_program.global_block().ops:
if op.type in opt_init_map.keys(
) and value_name == op.output("Out")[0]:
init_attr = [op.type]
for attr in opt_init_map[op.type]:
init_attr.append(str(op.attr(attr)))
value_attr.append("&".join(init_attr))
init_attrs.append(":".join(value_attr))
break
return "#".join(init_attrs)
kwargs = {}
kwargs["trainers"] = self.role_maker._worker_num()
kwargs["sparse_attrs"] = get_sparse_attrs()
return kwargs
from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_lr_ops, _has_global_step
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import \
SyncStrategy, GeoStrategy
trainer_config = self.async_strategy.get_trainer_runtime_config()
print(trainer_config)
dist_strategy = self.context["valid_strategy"]
launch_barrier = dist_strategy.a_sync_configs["launch_barrier"]
if launch_barrier:
# 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())
lrs = _has_global_step(_get_lr_ops(self.origin_main_program))
if lrs:
kwargs = {"need_global_step": "1"}
else:
kwargs = {"need_global_step": "0"}
if isinstance(self.async_strategy, GeoStrategy):
geo_kwargs = geo_strategy_envs()
kwargs.update(geo_kwargs)
if isinstance(self.async_strategy, SyncStrategy):
sync_kwargs = sync_strategy_envs()
kwargs.update(sync_kwargs)
kwargs = kwargs if kwargs else None
send_ctx = self.compiled_strategy.get_communicator_send_context()
if self.compiled_strategy.is_geo_mode():
recv_ctx = self.compiled_strategy.get_communicator_recv_context(
recv_type=4)
else:
recv_ctx = self.compiled_strategy.get_communicator_recv_context(
recv_type=1)
from paddle.fluid.communicator import Communicator
self._communicator = Communicator(
trainer_config.mode, kwargs,
trainer_config.get_communicator_flags())
self._communicator.init_with_ctx(send_ctx, recv_ctx)
if not self._communicator.is_running():
self._communicator.start()
else:
warnings.warn("communicator has been initialized, skip")
def _get_executor(self):
executor = fluid.Executor(fluid.CPUPlace())
if self.role_maker._is_heter_parameter_server_mode:
heter_worker_device_guard = self.context[
"valid_strategy"].a_sync_configs[
"heter_worker_device_guard"].upper()
if heter_worker_device_guard not in ["GPU", "XPU", "CPU"]:
raise ValueError("Heter Worker Not Support Device {}".format(
heter_worker_device_guard))
if self.role_maker._is_heter_worker():
if heter_worker_device_guard == "GPU":
executor = Executor(
fluid.CUDAPlace(
int(os.getenv("FLAGS_selected_gpus", "0"))))
elif heter_worker_device_guard == "XPU":
executor = Executor(
fluid.XPUPlace(
int(os.getenv("FLAGS_selected_xpus", "0"))))
return executor
def _init_server(self, *args, **kwargs):
if len(args) > 1:
raise ValueError("init server can only accept 1 args: `dirname`")
elif len(args) == 1:
model_dirname = args[0]
else:
model_dirname = None
executor = self._get_executor()
if self.role_maker._is_heter_worker(
) and self.context["valid_strategy"].a_sync_configs["launch_barrier"]:
# for heter trainer wait server ready
wait_server_ready(self.role_maker._get_pserver_endpoints())
executor.run(fluid.default_startup_program())
if self.role_maker._is_heter_worker():
self._init_worker()
return
sparse_varnames = self.compiled_strategy.get_sparse_varname_on_ps(
False)
sparse_related_optimize_varnames = []
for var_name in sparse_varnames:
sparse_related_optimize_varnames += self.compiled_strategy.get_optimize_varname_on_ps(
var_name)
sparse_related_optimize_varnames = list(
set(sparse_related_optimize_varnames))
distribtued_varnames = self.compiled_strategy.get_sparse_varname_on_ps(
True)
distributed_related_optimize_varnames = []
for var_name in distribtued_varnames:
distributed_related_optimize_varnames += self.compiled_strategy.get_optimize_varname_on_ps(
var_name)
distributed_related_optimize_varnames = list(
set(distributed_related_optimize_varnames))
remaining_vars = list(
filter(
ParameterServerRuntime.__exclude_vars(
sparse_varnames + distribtued_varnames +
sparse_related_optimize_varnames +
distributed_related_optimize_varnames),
fluid.default_main_program().list_vars()))
if not model_dirname:
return
if not os.path.isdir(model_dirname):
raise ValueError("There is no directory named '%s'", model_dirname)
# load dense
fluid.io.load_vars(executor,
main_program=fluid.default_main_program(),
dirname=model_dirname,
vars=remaining_vars)
# load sparse
self._load_sparse_params(executor=executor,
dirname=model_dirname,
varnames=sparse_varnames +
sparse_related_optimize_varnames)
# load large scale
self._load_distributed_params(dirname=model_dirname,
varnames=distribtued_varnames +
distributed_related_optimize_varnames)
def _run_server(self):
executor = self._get_executor()
executor.run(fluid.default_main_program())
def _stop_worker(self):
self._communicator.stop()
executor = self._get_executor()
executor.close()
def _get_optimizer_status(self, op, param_name):
supported_opts = [
"sgd", "adam", "adagrad", "adamax", "momentum", "lars_momentum",
"rmsprop", "decayed_adagrad", "ftrl"
]
reshaped_val_map = {}
reshaped_val_map["sgd"] = []
reshaped_val_map["adam"] = ["moment1_0", "moment2_0"]
reshaped_val_map["adagrad"] = ["moment_0"]
reshaped_val_map["adamax"] = ["moment_0", "inf_norm_0"]
reshaped_val_map["momentum"] = ["velocity_0"]
reshaped_val_map["lars_momentum"] = ["velocity_0"]
reshaped_val_map["rmsprop"] = [
"momentum_0", "mean_square_0", "mean_grad_0"
]
reshaped_val_map["decayed_adagrad"] = ["moment_0"]
reshaped_val_map["ftrl"] = ["squared_0", "linear_0"]
orishaped_val_map = {}
orishaped_val_map["adam"] = ["beta1_pow_acc_0", "beta2_pow_acc_0"]
orishaped_val_map["adamax"] = ["beta1_pow_acc_0"]
if op not in supported_opts:
raise ValueError(
"fleet can not support optimizer: {}, only this can be supported: {}"
.format(op, supported_opts))
reshaped_names = [
param_name + "_" + val for val in reshaped_val_map[op]
]
if op not in orishaped_val_map:
origin_names = []
else:
origin_names = [
param_name + "_" + val for val in orishaped_val_map[op]
]
return reshaped_names, origin_names
def _get_optimizer_op(self, param_name):
from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_optimize_ops
opts = _get_optimize_ops(self.origin_main_program)
for op in opts:
if "Param" in op.input_names and \
"LearningRate" in op.input_names and op.input("Param")[0] == param_name:
return op
def _save_dense_params(self, executor, dirname, context, main_program):
self._communicator.recv()
prog = Program()
block = prog.global_block()
local_vars = []
for name, var_ctx in context.items():
if len(var_ctx.origin_varnames()) != 1:
raise ValueError("Dense can not support split now.")
varname = var_ctx.origin_varnames()[0]
local_vars.append(varname)
optimizer = self._get_optimizer_op(varname)
reshaped_varnames, origin_varnames = self._get_optimizer_status(
optimizer.type, varname)
for var_name in [varname] + reshaped_varnames + origin_varnames:
var = self.origin_main_program.global_block().vars[var_name]
block.append_op(type='recv_save',
attrs={
"trainer_id":
self.role_maker._worker_index(),
"shape":
var.shape,
"slice_shapes":
[",".join([str(i) for i in var.shape])],
"slice_varnames": [var.name],
"remote_varnames": [var.name],
"is_sparse":
False,
"endpoints":
var_ctx.split_endpoints(),
"file_path":
os.path.join(dirname, var.name)
})
executor.run(prog)
return local_vars
def _save_sparse_params(self, executor, dirname, context, main_program):
prog = Program()
block = prog.global_block()
local_vars = []
for name, var_ctx in context.items():
if len(var_ctx.origin_varnames()) != 1:
raise ValueError("Dense can not support split now.")
varname = var_ctx.origin_varnames()[0]
local_vars.append(varname)
optimizer = self._get_optimizer_op(varname)
reshaped_varnames, origin_varnames = self._get_optimizer_status(
optimizer.type, varname)
var = self.origin_main_program.global_block().vars[varname]
slice_shapes = []
dims1 = ",".join([str(i) for i in var.shape[1:]])
for section in var_ctx.sections():
slice_shapes.append(str(section) + dims1)
block.append_op(type='recv_save',
attrs={
"trainer_id":
self.role_maker._worker_index(),
"shape":
var.shape,
"slice_shapes":
slice_shapes,
"slice_varnames":
var_ctx.split_varnames(),
"remote_varnames":
var_ctx.split_varnames(),
"is_sparse":
True,
"endpoints":
var_ctx.split_endpoints(),
"pserver_num":
len(self.role_maker._get_pserver_endpoints()),
"file_path":
os.path.join(dirname, var.name)
})
for reshaped_varname in reshaped_varnames:
var = self.origin_main_program.global_block(
).vars[reshaped_varname]
slice_varnames = []
remote_varnames = []
for i in range(len(var_ctx.split_varnames())):
slice_varnames.append("{}.block{}".format(
reshaped_varname, i))
remote_varnames.append(reshaped_varname)
block.append_op(
type='recv_save',
attrs={
"trainer_id":
self.role_maker._worker_index(),
"shape":
var.shape,
"slice_shapes":
slice_shapes,
"slice_varnames":
slice_varnames,
"remote_varnames":
remote_varnames,
"is_sparse":
True,
"endpoints":
var_ctx.split_endpoints(),
"pserver_num":
len(self.role_maker._get_pserver_endpoints()),
"file_path":
os.path.join(dirname, var.name)
})
for origin_varname in origin_varnames:
var = self.origin_main_program.global_block(
).vars[origin_varname]
block.append_op(type='recv_save',
attrs={
"trainer_id":
self.role_maker._worker_index(),
"shape":
var.shape,
"slice_shapes":
[",".join([str(i) for i in var.shape])],
"slice_varnames": [origin_varname],
"remote_varnames": [origin_varname],
"is_sparse":
False,
"endpoints":
var_ctx.split_endpoints()[:1],
"file_path":
os.path.join(dirname, var.name)
})
executor.run(prog)
return context.keys()
def _save_distributed_params(self, executor, dirname, context, mode):
prog = Program()
block = prog.global_block()
for name, var_ctx in context.items():
block.append_op(type='checkpoint_notify',
attrs={
"varname": name,
"mode": mode,
"slice_varnames": var_ctx.split_varnames(),
"remote_varnames": var_ctx.split_varnames(),
"endpoints": var_ctx.split_endpoints(),
"dirname": dirname
})
executor.run(prog)
return context.keys()
def _save_distributed_persistables(self, executor, dirname, main_program,
mode):
dense_ctx = self.compiled_strategy.get_communicator_recv_context(
recv_type=1, use_origin_program=True)
sparse_ctx = self.compiled_strategy.get_communicator_recv_context(
recv_type=2, use_origin_program=True)
distributed_ctx = self.compiled_strategy.get_communicator_recv_context(
recv_type=3, use_origin_program=True)
recv_dense_varnames = self._save_dense_params(executor, dirname,
dense_ctx, main_program)
recv_sparse_varnames = self._save_sparse_params(
executor, dirname, sparse_ctx, main_program)
recv_distributed_varnames = self._save_distributed_params(
executor, dirname, distributed_ctx, mode)
saved_varnames = recv_dense_varnames + list(
recv_sparse_varnames) + list(recv_distributed_varnames)
remaining_vars = list(
filter(ParameterServerRuntime.__exclude_vars(saved_varnames),
main_program.list_vars()))
fluid.io.save_vars(executor,
main_program=main_program,
dirname=dirname,
vars=remaining_vars)
def _ps_inference_save_persistables(self,
executor,
dirname,
main_program=None,
mode=0,
**kwargs):
"""
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.compiled_strategy.get_origin_ps_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"
)
self._save_distributed_persistables(executor, dirname, main_program,
mode)
def _ps_inference_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"
)
fluid.io.save_inference_model(dirname, feeded_var_names,
target_vars, executor, main_program,
None, None, export_for_deployment)
else:
fluid.io.save_inference_model(dirname, feeded_var_names,
target_vars, executor,
self.origin_main_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(fluid.default_main_program())
self._ps_inference_save_persistables(executor,
dirname,
program,
mode=0)
def _save_inference_model(self, *args, **kwargs):
self._ps_inference_save_inference_model(*args, **kwargs)
def _save_persistables(self, *args, **kwargs):
self._ps_inference_save_persistables(*args, **kwargs)
class FleetTranspiler(Fleet):
"""
A subclass for compatibility with fluid.transpiler.DistributeTranspiler.
"""
def __init__(self):
super(FleetTranspiler, self).__init__(Mode.TRANSPILER)
self._inner_mode = None
if version.is_transpiler():
self._inner_mode = PSMode.TRANSPILER
else:
self._inner_mode = PSMode.PSLIB
self._strategy = None
self._transpiler = None
self._origin_main_program = None
self._origin_startup_program = None
self._communicator = None
self.startup_program = None
self.main_program = None
self._opt_info = None
self._local_ip = 0
self._fleet_ptr = None
self._main_programs = []
self._scopes = []
self._client2client_request_timeout_ms = 500000
self._client2client_connect_timeout_ms = 10000
self._client2client_max_retry = 3
def init(self, role_maker=None):
if role_maker is None:
role_maker = MPISymetricRoleMaker()
super(FleetTranspiler, self).init(role_maker)
self._fleet_ptr = core.Fleet()
def _init_transpiler_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
"""
def sync_strategy_envs():
kwargs = {}
kwargs[
"pserver_endpoints"] = self._role_maker.get_pserver_endpoints(
)
kwargs["trainer_id"] = self._role_maker.worker_index()
return kwargs
def geo_strategy_envs():
def get_sparse_attrs():
opt_init_map = {}
opt_init_map["gaussian_random"] = ["seed", "mean", "std"]
opt_init_map["fill_constant"] = ["value"]
opt_init_map["uniform_random"] = ["seed", "min", "max"]
opt_init_map["truncated_gaussian_random"] = [
"seed", "mean", "std"
]
dist_varnames = get_sparse_tablenames(
self._origin_main_program, True)
sparse_varnames = get_sparse_tablenames(
self._origin_main_program, False)
if len(dist_varnames) != 0:
raise ValueError(
"GeoStrategy can not support large scale embeding now, please use fluid.layers.embedding"
)
init_attrs = []
for value_name in sparse_varnames:
value_var = self._origin_main_program.global_block(
).vars[value_name]
value_attr = [
value_name,
",".join([str(dim) for dim in value_var.shape])
]
for op in self._origin_startup_program.global_block().ops:
if op.type in opt_init_map.keys(
) and value_name == op.output("Out")[0]:
init_attr = [op.type]
for attr in opt_init_map[op.type]:
init_attr.append(str(op.attr(attr)))
value_attr.append("&".join(init_attr))
init_attrs.append(":".join(value_attr))
break
return "#".join(init_attrs)
kwargs = {}
kwargs["trainers"] = self.worker_num()
kwargs["sparse_attrs"] = get_sparse_attrs()
return kwargs
# 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
wait_server_ready(self.server_endpoints(to_string=False))
trainer_config = self._strategy.get_trainer_runtime_config()
print(trainer_config)
lrs = _has_global_step(_get_lr_ops(self._origin_main_program))
if lrs > 0:
kwargs = {"need_global_step": "1"}
else:
kwargs = {"need_global_step": "0"}
if isinstance(self._strategy, GeoStrategy):
geo_kwargs = geo_strategy_envs()
kwargs.update(geo_kwargs)
if isinstance(self._strategy, SyncStrategy):
sync_kwargs = sync_strategy_envs()
kwargs.update(sync_kwargs)
kwargs = kwargs if kwargs else None
send_ctx = fleet.compiled_config.get_communicator_send_context()
if self.compiled_config.is_geo_mode():
recv_ctx = fleet.compiled_config.get_communicator_recv_context(
recv_type=4)
else:
recv_ctx = fleet.compiled_config.get_communicator_recv_context(
recv_type=1)
from paddle.fluid.communicator import Communicator
self._communicator = Communicator(
trainer_config.mode, kwargs,
trainer_config.get_communicator_flags())
self._communicator.init_with_ctx(send_ctx, recv_ctx)
if not self._communicator.is_running():
self._communicator.start()
else:
raise ValueError(
"Communicator can only be inited once, please check")
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 self._inner_mode == PSMode.TRANSPILER:
self._init_transpiler_worker()
else:
raise NotImplementedError("add implement later")
def _init_transpiler_server(self, model_dir=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)
sparse_varnames = self.compiled_config.get_sparse_varname_on_ps(
True)
distribtued_varnames = self.compiled_config.get_sparse_varname_on_ps(
False)
remaining_vars = list(
filter(
FleetTranspiler.__exclude_vars(sparse_varnames +
distribtued_varnames),
self.main_program.list_vars()))
fluid.io.load_vars(self._executor,
main_program=self.main_program,
dirname=model_dir,
vars=remaining_vars)
self._load_sparse_params(dirname=model_dir,
varnames=sparse_varnames)
# todo(tangwei12) load distributed vars
# self._load_sparse_params(dirname=model_dir, varnames=distribtued_varnames)
def init_server(self, model_dir=None, **kwargs):
"""
`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 self._inner_mode == PSMode.TRANSPILER:
self._init_transpiler_server(model_dir)
else:
raise NotImplementedError("add implement later")
def run_server(self):
"""
`run_server` execute executor to start pserver main program.
Returns:
None
"""
if self._inner_mode == PSMode.TRANSPILER:
if not self.main_program:
raise ValueError(
"main_program is None, need invoke DistributedOptimizer.minimize first"
)
self._executor.run(self.main_program)
else:
raise NotImplementedError("add implement later")
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 self._inner_mode == PSMode.TRANSPILER:
self._communicator.stop()
if isinstance(self._role_maker, MPISymetricRoleMaker):
self._role_maker._finalize()
self._executor.close()
else:
raise NotImplementedError("add implement later")
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")
if not self._is_initialized:
raise ValueError(
"fleet.init(role) to initialize before optimizer.minimize(loss)"
)
if not strategy:
_strategy = StrategyFactory.create_async_strategy()
if isinstance(strategy, DistributedStrategy):
_strategy = strategy
elif isinstance(strategy, DistributeTranspilerConfig):
if strategy.sync_mode:
_strategy = SyncStrategy()
else:
if strategy.runtime_split_send_recv:
if strategy.geo_sgd_mode:
_strategy = GeoStrategy(
strategy.geo_sgd_need_push_nums)
elif strategy.half_async:
_strategy = HalfAsyncStrategy()
else:
_strategy = AsyncStrategy()
else:
_strategy = HalfAsyncStrategy()
# for half_async compatibility
strategy.half_async = True
strategy.runtime_split_send_recv = True
_strategy.set_program_config(strategy)
elif isinstance(strategy, dict):
if self._inner_mode != PSMode.PSLIB:
raise TypeError("Dict strategy can only be used at PSLIB Mode")
_strategy = StrategyFactory.create_async_strategy()
_strategy.set_pslib_runtime_config(strategy)
else:
raise TypeError(
"strategy must be an instance of DistributeTranspilerConfig, DistributedStrategy"
)
self._strategy = _strategy
self._optimizer = ParameterServerOptimizer(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 self._inner_mode == PSMode.PSLIB:
raise NotImplementedError("add implement later")
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"
)
# Todo(MrChengmo): support recv&save GPU-Kernel for ps-gpu model save
if not isinstance(executor.place, fluid.CPUPlace):
save_executor = Executor(fluid.CPUPlace())
else:
save_executor = executor
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"
)
fluid.io.save_inference_model(dirname, feeded_var_names,
target_vars, executor, main_program,
None, None, export_for_deployment)
else:
fluid.io.save_inference_model(dirname, feeded_var_names,
target_vars, executor,
self._origin_main_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 _load_sparse_params(self, dirname, varnames):
from paddle.fluid.communicator import LargeScaleKV
scale_kv = LargeScaleKV()
for varname in varnames:
origin_varname, _, _ = public._get_varname_parts(varname)
sparse_dir = os.path.join(dirname, origin_varname, varname)
scale_kv.load(varname, sparse_dir)
def _get_optimizer_status(self, op, param_name):
supported_opts = [
"sgd", "adam", "adagrad", "adamax", "momentum", "lars_momentum",
"rmsprop", "decayed_adagrad", "ftrl"
]
reshaped_val_map = {}
reshaped_val_map["sgd"] = []
reshaped_val_map["adam"] = ["moment1_0", "moment2_0"]
reshaped_val_map["adagrad"] = ["moment_0"]
reshaped_val_map["adamax"] = ["moment_0", "inf_norm_0"]
reshaped_val_map["momentum"] = ["velocity_0"]
reshaped_val_map["lars_momentum"] = ["velocity_0"]
reshaped_val_map["rmsprop"] = [
"momentum_0", "mean_square_0", "mean_grad_0"
]
reshaped_val_map["decayed_adagrad"] = ["moment_0"]
reshaped_val_map["ftrl"] = ["squared_0", "linear_0"]
orishaped_val_map = {}
orishaped_val_map["adam"] = ["beta1_pow_acc_0", "beta2_pow_acc_0"]
orishaped_val_map["adamax"] = ["beta1_pow_acc_0"]
if op not in supported_opts:
raise ValueError(
"fleet can not support optimizer: {}, only this can be supported: {}"
.format(op, supported_opts))
reshaped_names = [
param_name + "_" + val for val in reshaped_val_map[op]
]
if op not in orishaped_val_map:
origin_names = []
else:
origin_names = [
param_name + "_" + val for val in orishaped_val_map[op]
]
return reshaped_names, origin_names
def _get_optimizer_op(self, param_name):
opts = public._get_optimize_ops(self._origin_main_program)
for op in opts:
if "Param" in op.input_names and \
"LearningRate" in op.input_names and op.input("Param")[0] == param_name:
return op
def _save_dense_params(self, executor, dirname, context, main_program):
self._communicator.recv()
prog = Program()
block = prog.global_block()
local_vars = []
for name, var_ctx in context.items():
if len(var_ctx.origin_varnames()) != 1:
raise ValueError("Dense can not support split now.")
varname = var_ctx.origin_varnames()[0]
local_vars.append(varname)
optimizer = self._get_optimizer_op(varname)
reshaped_varnames, origin_varnames = self._get_optimizer_status(
optimizer.type, varname)
for var_name in [varname] + reshaped_varnames + origin_varnames:
var = self._origin_main_program.global_block().vars[var_name]
block.append_op(type='recv_save',
attrs={
"trainer_id":
self._role_maker.worker_index(),
"shape":
var.shape,
"slice_shapes":
[",".join([str(i) for i in var.shape])],
"slice_varnames": [var.name],
"remote_varnames": [var.name],
"is_sparse":
False,
"endpoints":
var_ctx.split_endpoints(),
"file_path":
os.path.join(dirname, var.name)
})
executor.run(prog)
return local_vars
def _save_sparse_params(self, executor, dirname, context, main_program):
prog = Program()
block = prog.global_block()
local_vars = []
for name, var_ctx in context.items():
if len(var_ctx.origin_varnames()) != 1:
raise ValueError("Dense can not support split now.")
varname = var_ctx.origin_varnames()[0]
local_vars.append(varname)
optimizer = self._get_optimizer_op(varname)
reshaped_varnames, origin_varnames = self._get_optimizer_status(
optimizer.type, varname)
var = self._origin_main_program.global_block().vars[varname]
slice_shapes = []
dims1 = ",".join([str(i) for i in var.shape[1:]])
for section in var_ctx.sections():
slice_shapes.append(str(section) + dims1)
block.append_op(type='recv_save',
attrs={
"trainer_id":
self._role_maker.worker_index(),
"shape":
var.shape,
"slice_shapes":
slice_shapes,
"slice_varnames":
var_ctx.split_varnames(),
"remote_varnames":
var_ctx.split_varnames(),
"is_sparse":
True,
"endpoints":
var_ctx.split_endpoints(),
"pserver_num":
len(self._role_maker.get_pserver_endpoints()),
"file_path":
os.path.join(dirname, var.name)
})
for reshaped_varname in reshaped_varnames:
var = self._origin_main_program.global_block(
).vars[reshaped_varname]
slice_varnames = []
remote_varnames = []
for i in range(len(var_ctx.split_varnames())):
slice_varnames.append("{}.block{}".format(
reshaped_varname, i))
remote_varnames.append(reshaped_varname)
block.append_op(
type='recv_save',
attrs={
"trainer_id":
self._role_maker.worker_index(),
"shape":
var.shape,
"slice_shapes":
slice_shapes,
"slice_varnames":
slice_varnames,
"remote_varnames":
remote_varnames,
"is_sparse":
True,
"endpoints":
var_ctx.split_endpoints(),
"pserver_num":
len(self._role_maker.get_pserver_endpoints()),
"file_path":
os.path.join(dirname, var.name)
})
for origin_varname in origin_varnames:
var = self._origin_main_program.global_block(
).vars[origin_varname]
block.append_op(type='recv_save',
attrs={
"trainer_id":
self._role_maker.worker_index(),
"shape":
var.shape,
"slice_shapes":
[",".join([str(i) for i in var.shape])],
"slice_varnames": [origin_varname],
"remote_varnames": [origin_varname],
"is_sparse":
False,
"endpoints":
var_ctx.split_endpoints()[:1],
"file_path":
os.path.join(dirname, var.name)
})
executor.run(prog)
return context.keys()
def _save_distributed_params(self, executor, dirname, context,
main_program):
prog = Program()
block = prog.global_block()
for name, var_ctx in context.items():
block.append_op(type='checkpoint_notify',
attrs={
"varname": name,
"is_slice": True,
"slice_varnames": var_ctx.split_varnames(),
"remote_varnames": var_ctx.split_varnames(),
"endpoints": var_ctx.split_endpoints(),
"dirname": dirname
})
executor.run(prog)
return context.keys()
def _save_distributed_persistables(self, executor, dirname, main_program):
dense_ctx = fleet.compiled_config.get_communicator_recv_context(
recv_type=1)
sparse_ctx = fleet.compiled_config.get_communicator_recv_context(
recv_type=2)
distributed_ctx = fleet.compiled_config.get_communicator_recv_context(
recv_type=3)
recv_dense_varnames = self._save_dense_params(executor, dirname,
dense_ctx, main_program)
recv_sparse_varnames = self._save_sparse_params(
executor, dirname, sparse_ctx, main_program)
recv_distributed_varnames = self._save_distributed_params(
executor, dirname, distributed_ctx, main_program)
saved_varnames = recv_dense_varnames + list(
recv_sparse_varnames) + list(recv_distributed_varnames)
remaining_vars = list(
filter(FleetTranspiler.__exclude_vars(saved_varnames),
main_program.list_vars()))
fluid.io.save_vars(executor,
main_program=main_program,
dirname=dirname,
vars=remaining_vars)
def save_persistables(self,
executor,
dirname,
main_program=None,
**kwargs):
"""
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 self._inner_mode == PSMode.PSLIB:
raise NotImplementedError("add implement later")
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"
)
# Todo(MrChengmo): support recv&save GPU-Kernel for ps-gpu model save
if not isinstance(executor.place, fluid.CPUPlace):
save_executor = Executor(fluid.CPUPlace())
else:
save_executor = executor
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"
)
self._save_distributed_persistables(save_executor, dirname,
main_program)
@staticmethod
def __exclude_vars(exclude_var_names=[]):
def is_valid(var):
if var.name in exclude_var_names:
return False
origin_varname, _, _ = public._get_varname_parts(var.name)
if origin_varname.endswith("@GRAD"):
return False
if origin_varname == "learning_rate_0":
return False
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == core.VarDesc.VarType.READER:
return False
return var.persistable
return is_valid
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()])