You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Paddle/python/paddle/distributed/fleet/runtime/parameter_server_runtime.py

664 lines
26 KiB

# Copyright (c) 2020 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.
import os
import warnings
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.framework import Program
from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.executor import Executor
from paddle.fluid.parallel_executor import ParallelExecutor
from paddle.fluid.framework import Variable, Parameter
from .runtime_base import RuntimeBase
from ..base.private_helper_function import wait_server_ready
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()
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,
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 = 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, main_program)
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,
**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)
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)
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)