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664 lines
26 KiB
664 lines
26 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import warnings
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import paddle.fluid as fluid
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from paddle.fluid import core
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from paddle.fluid.framework import Program
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from paddle.fluid.compiler import CompiledProgram
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from paddle.fluid.executor import Executor
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from paddle.fluid.parallel_executor import ParallelExecutor
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from paddle.fluid.framework import Variable, Parameter
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from .runtime_base import RuntimeBase
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from ..base.private_helper_function import wait_server_ready
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class ParameterServerRuntime(RuntimeBase):
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def __init__(self):
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super(ParameterServerRuntime, self).__init__()
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self._communicator = None
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def _set_basic_info(self, context):
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self.context = context
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self.role_maker = context["role_maker"]
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self.origin_main_program = context["origin_main_program"]
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self.origin_startup_program = context["origin_startup_program"]
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self.async_strategy = self._get_distributed_strategy()
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self.compiled_strategy = self.build_compiled_startegy()
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def _get_distributed_strategy(self):
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strategy = None
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
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dist_strategy = self.context["valid_strategy"]
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k_steps = dist_strategy.a_sync_configs["k_steps"]
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if not dist_strategy.a_sync and k_steps == 0:
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strategy = StrategyFactory.create_sync_strategy()
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if dist_strategy.a_sync and k_steps == 0:
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strategy = StrategyFactory.create_async_strategy()
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if dist_strategy.a_sync and k_steps > 0:
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strategy = StrategyFactory.create_geo_strategy(k_steps)
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if not strategy:
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raise ValueError("k_steps must be invalid value, please check")
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return strategy
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def build_compiled_startegy(self):
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import CompileTimeStrategy
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compiled_config = CompileTimeStrategy(
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self.origin_main_program, self.origin_main_program,
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self.async_strategy, self.role_maker)
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return compiled_config
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def _load_sparse_params(self,
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executor,
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dirname,
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varnames,
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main_program=None):
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assert vars != None
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check_vars = []
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load_prog = Program()
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load_block = load_prog.global_block()
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def _in_varnames(var):
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return var.name in varnames
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load_vars = list(
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filter(_in_varnames, fluid.default_main_program().list_vars()))
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if main_program is None:
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main_program = self.origin_main_program
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
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for each_var in load_vars:
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assert isinstance(each_var, Variable)
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origin_varname, _, _ = _get_varname_parts(each_var.name)
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new_var = fluid.io._clone_var_in_block_(load_block, each_var)
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var_path = os.path.join(dirname, origin_varname)
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if not os.path.exists(var_path):
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raise ValueError("SelectedRows var {} can not find at {}".
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format(new_var.name, var_path))
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if os.path.isfile(var_path):
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load_block.append_op(
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type='sparse_tensor_load',
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inputs={},
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outputs={'Out': [new_var]},
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attrs={
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'file_path': os.path.join(dirname, origin_varname),
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'node_index': self.role_maker._server_index(),
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'node_num': self.role_maker._server_num(),
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'shape': each_var.shape
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})
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check_vars.append(each_var)
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executor.run(load_prog)
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def _load_distributed_params(self, dirname, varnames):
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from paddle.fluid.communicator import LargeScaleKV
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
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scale_kv = LargeScaleKV()
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for varname in varnames:
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origin_varname, _, _ = _get_varname_parts(varname)
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sparse_dir = os.path.join(dirname, origin_varname, varname)
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scale_kv.load(varname, sparse_dir)
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@staticmethod
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def __exclude_vars(exclude_var_names=[]):
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def is_valid(var):
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if var.name in exclude_var_names:
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return False
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_varname_parts
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origin_varname, _, _ = _get_varname_parts(var.name)
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if origin_varname.endswith("@GRAD"):
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return False
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if origin_varname == "learning_rate_0":
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return False
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if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
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var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
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var.desc.type() == core.VarDesc.VarType.READER:
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return False
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return var.persistable
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return is_valid
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def _init_worker(self):
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def sync_strategy_envs():
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kwargs = {}
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kwargs[
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"pserver_endpoints"] = self.role_maker._get_pserver_endpoints()
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kwargs["trainer_id"] = self.role_maker._worker_index()
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return kwargs
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def geo_strategy_envs():
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import get_sparse_tablenames
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def get_sparse_attrs():
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opt_init_map = {}
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opt_init_map["gaussian_random"] = ["seed", "mean", "std"]
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opt_init_map["fill_constant"] = ["value"]
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opt_init_map["uniform_random"] = ["seed", "min", "max"]
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opt_init_map[
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"truncated_gaussian_random"] = ["seed", "mean", "std"]
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dist_varnames = get_sparse_tablenames(self.origin_main_program,
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True)
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sparse_varnames = get_sparse_tablenames(
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self.origin_main_program, False)
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if len(dist_varnames) != 0:
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raise ValueError(
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"GeoStrategy can not support large scale embeding now, please use fluid.layers.embedding"
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)
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init_attrs = []
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for value_name in sparse_varnames:
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value_var = self.origin_main_program.global_block().vars[
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value_name]
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value_attr = [
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value_name,
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",".join([str(dim) for dim in value_var.shape])
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]
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for op in self.origin_startup_program.global_block().ops:
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if op.type in opt_init_map.keys(
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) and value_name == op.output("Out")[0]:
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init_attr = [op.type]
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for attr in opt_init_map[op.type]:
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init_attr.append(str(op.attr(attr)))
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value_attr.append("&".join(init_attr))
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init_attrs.append(":".join(value_attr))
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break
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return "#".join(init_attrs)
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kwargs = {}
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kwargs["trainers"] = self.role_maker._worker_num()
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kwargs["sparse_attrs"] = get_sparse_attrs()
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return kwargs
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_lr_ops, _has_global_step
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from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import \
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SyncStrategy, GeoStrategy
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trainer_config = self.async_strategy.get_trainer_runtime_config()
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dist_strategy = self.context["valid_strategy"]
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launch_barrier = dist_strategy.a_sync_configs["launch_barrier"]
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if launch_barrier:
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# for trainer wait server ready
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wait_server_ready(self.role_maker._get_pserver_endpoints())
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# for ps-heter mode, wait heter worker ready
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if self.role_maker._is_heter_parameter_server_mode and self.role_maker._is_worker(
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):
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wait_server_ready(self.role_maker._get_heter_worker_endpoints())
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lrs = _has_global_step(_get_lr_ops(self.origin_main_program))
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if lrs:
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kwargs = {"need_global_step": "1"}
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else:
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kwargs = {"need_global_step": "0"}
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if isinstance(self.async_strategy, GeoStrategy):
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geo_kwargs = geo_strategy_envs()
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kwargs.update(geo_kwargs)
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if isinstance(self.async_strategy, SyncStrategy):
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sync_kwargs = sync_strategy_envs()
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kwargs.update(sync_kwargs)
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kwargs = kwargs if kwargs else None
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send_ctx = self.compiled_strategy.get_communicator_send_context()
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if self.compiled_strategy.is_geo_mode():
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recv_ctx = self.compiled_strategy.get_communicator_recv_context(
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recv_type=4)
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else:
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recv_ctx = self.compiled_strategy.get_communicator_recv_context(
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recv_type=1)
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from paddle.fluid.communicator import Communicator
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self._communicator = Communicator(
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trainer_config.mode, kwargs,
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trainer_config.get_communicator_flags())
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self._communicator.init_with_ctx(send_ctx, recv_ctx)
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if not self._communicator.is_running():
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self._communicator.start()
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else:
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warnings.warn("communicator has been initialized, skip")
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def _get_executor(self):
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executor = fluid.Executor(fluid.CPUPlace())
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if self.role_maker._is_heter_parameter_server_mode:
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heter_worker_device_guard = self.context[
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"valid_strategy"].a_sync_configs[
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"heter_worker_device_guard"].upper()
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if heter_worker_device_guard not in ["GPU", "XPU", "CPU"]:
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raise ValueError("Heter Worker Not Support Device {}".format(
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heter_worker_device_guard))
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if self.role_maker._is_heter_worker():
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if heter_worker_device_guard == "GPU":
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executor = Executor(
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fluid.CUDAPlace(
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int(os.getenv("FLAGS_selected_gpus", "0"))))
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elif heter_worker_device_guard == "XPU":
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executor = Executor(
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fluid.XPUPlace(
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int(os.getenv("FLAGS_selected_xpus", "0"))))
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return executor
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def _init_server(self, *args, **kwargs):
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if len(args) > 1:
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raise ValueError("init server can only accept 1 args: `dirname`")
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elif len(args) == 1:
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model_dirname = args[0]
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else:
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model_dirname = None
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executor = self._get_executor()
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if self.role_maker._is_heter_worker() and self.context[
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"valid_strategy"].a_sync_configs["launch_barrier"]:
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# for heter trainer wait server ready
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wait_server_ready(self.role_maker._get_pserver_endpoints())
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executor.run(fluid.default_startup_program())
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if self.role_maker._is_heter_worker():
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self._init_worker()
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return
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sparse_varnames = self.compiled_strategy.get_sparse_varname_on_ps(False)
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sparse_related_optimize_varnames = []
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for var_name in sparse_varnames:
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sparse_related_optimize_varnames += self.compiled_strategy.get_optimize_varname_on_ps(
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var_name)
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sparse_related_optimize_varnames = list(
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set(sparse_related_optimize_varnames))
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distribtued_varnames = self.compiled_strategy.get_sparse_varname_on_ps(
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True)
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distributed_related_optimize_varnames = []
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for var_name in distribtued_varnames:
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distributed_related_optimize_varnames += self.compiled_strategy.get_optimize_varname_on_ps(
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var_name)
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distributed_related_optimize_varnames = list(
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set(distributed_related_optimize_varnames))
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remaining_vars = list(
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filter(
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ParameterServerRuntime.__exclude_vars(
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sparse_varnames + distribtued_varnames +
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sparse_related_optimize_varnames +
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distributed_related_optimize_varnames),
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fluid.default_main_program().list_vars()))
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if not model_dirname:
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return
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if not os.path.isdir(model_dirname):
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raise ValueError("There is no directory named '%s'", model_dirname)
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# load dense
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fluid.io.load_vars(
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executor,
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main_program=fluid.default_main_program(),
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dirname=model_dirname,
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vars=remaining_vars)
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# load sparse
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self._load_sparse_params(
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executor=executor,
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dirname=model_dirname,
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varnames=sparse_varnames + sparse_related_optimize_varnames)
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# load large scale
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self._load_distributed_params(
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dirname=model_dirname,
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varnames=distribtued_varnames +
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distributed_related_optimize_varnames)
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def _run_server(self):
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executor = self._get_executor()
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executor.run(fluid.default_main_program())
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def _stop_worker(self):
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self._communicator.stop()
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executor = self._get_executor()
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executor.close()
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def _get_optimizer_status(self, op, param_name):
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supported_opts = [
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"sgd", "adam", "adagrad", "adamax", "momentum", "lars_momentum",
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"rmsprop", "decayed_adagrad", "ftrl"
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]
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reshaped_val_map = {}
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reshaped_val_map["sgd"] = []
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reshaped_val_map["adam"] = ["moment1_0", "moment2_0"]
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reshaped_val_map["adagrad"] = ["moment_0"]
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reshaped_val_map["adamax"] = ["moment_0", "inf_norm_0"]
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reshaped_val_map["momentum"] = ["velocity_0"]
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reshaped_val_map["lars_momentum"] = ["velocity_0"]
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reshaped_val_map[
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"rmsprop"] = ["momentum_0", "mean_square_0", "mean_grad_0"]
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reshaped_val_map["decayed_adagrad"] = ["moment_0"]
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reshaped_val_map["ftrl"] = ["squared_0", "linear_0"]
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orishaped_val_map = {}
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orishaped_val_map["adam"] = ["beta1_pow_acc_0", "beta2_pow_acc_0"]
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orishaped_val_map["adamax"] = ["beta1_pow_acc_0"]
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if op not in supported_opts:
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raise ValueError(
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"fleet can not support optimizer: {}, only this can be supported: {}".
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format(op, supported_opts))
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reshaped_names = [
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param_name + "_" + val for val in reshaped_val_map[op]
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]
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if op not in orishaped_val_map:
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origin_names = []
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else:
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origin_names = [
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param_name + "_" + val for val in orishaped_val_map[op]
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]
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return reshaped_names, origin_names
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def _get_optimizer_op(self, param_name):
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from paddle.fluid.incubate.fleet.parameter_server.ir.public import _get_optimize_ops
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opts = _get_optimize_ops(self.origin_main_program)
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for op in opts:
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if "Param" in op.input_names and \
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"LearningRate" in op.input_names and op.input("Param")[0] == param_name:
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return op
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def _save_dense_params(self, executor, dirname, context, main_program):
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self._communicator.recv()
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prog = Program()
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block = prog.global_block()
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local_vars = []
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for name, var_ctx in context.items():
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if len(var_ctx.origin_varnames()) != 1:
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raise ValueError("Dense can not support split now.")
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varname = var_ctx.origin_varnames()[0]
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local_vars.append(varname)
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optimizer = self._get_optimizer_op(varname)
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reshaped_varnames, origin_varnames = self._get_optimizer_status(
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optimizer.type, varname)
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for var_name in [varname] + reshaped_varnames + origin_varnames:
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var = self.origin_main_program.global_block().vars[var_name]
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block.append_op(
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type='recv_save',
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attrs={
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"trainer_id": self.role_maker._worker_index(),
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"shape": var.shape,
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"slice_shapes":
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[",".join([str(i) for i in var.shape])],
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"slice_varnames": [var.name],
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"remote_varnames": [var.name],
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"is_sparse": False,
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"endpoints": var_ctx.split_endpoints(),
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"file_path": os.path.join(dirname, var.name)
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})
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executor.run(prog)
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return local_vars
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def _save_sparse_params(self, executor, dirname, context, main_program):
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prog = Program()
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block = prog.global_block()
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local_vars = []
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for name, var_ctx in context.items():
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if len(var_ctx.origin_varnames()) != 1:
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raise ValueError("Dense can not support split now.")
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varname = var_ctx.origin_varnames()[0]
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local_vars.append(varname)
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optimizer = self._get_optimizer_op(varname)
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reshaped_varnames, origin_varnames = self._get_optimizer_status(
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optimizer.type, varname)
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var = self.origin_main_program.global_block().vars[varname]
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slice_shapes = []
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dims1 = ",".join([str(i) for i in var.shape[1:]])
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for section in var_ctx.sections():
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slice_shapes.append(str(section) + dims1)
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block.append_op(
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type='recv_save',
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attrs={
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"trainer_id": self.role_maker._worker_index(),
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"shape": var.shape,
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"slice_shapes": slice_shapes,
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"slice_varnames": var_ctx.split_varnames(),
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"remote_varnames": var_ctx.split_varnames(),
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"is_sparse": True,
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"endpoints": var_ctx.split_endpoints(),
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"pserver_num":
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len(self.role_maker._get_pserver_endpoints()),
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"file_path": os.path.join(dirname, var.name)
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})
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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)
|