wehub-resource-sync
767 lines
27 KiB
Python
767 lines
27 KiB
Python
# 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
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from paddle.framework import core
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from paddle.static import (
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CompiledProgram,
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Executor,
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Program,
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Variable,
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default_main_program,
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default_startup_program,
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)
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from ..base.private_helper_function import wait_server_ready
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from .runtime_base import RuntimeBase
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__all__ = []
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class ParameterServerRuntime(RuntimeBase):
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def __init__(self):
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super().__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_strategy()
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def _get_distributed_strategy(self):
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strategy = None
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from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler.distributed_strategy import (
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StrategyFactory,
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)
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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_strategy(self):
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from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
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CompileTimeStrategy,
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)
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compiled_config = CompileTimeStrategy(
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self.origin_main_program,
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self.origin_main_program,
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self.async_strategy,
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self.role_maker,
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)
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return compiled_config
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def _load_sparse_params(
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self, executor, dirname, varnames, main_program=None
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):
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assert vars is not 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, default_main_program().list_vars())
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)
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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.incubate.distributed.fleet.parameter_server.ir.public import (
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_get_varname_parts,
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)
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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 = paddle.static.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(
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f"SelectedRows var {new_var.name} can not find at {var_path}"
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)
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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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)
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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.distributed.communicator import LargeScaleKV
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from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
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_get_varname_parts,
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)
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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.incubate.distributed.fleet.parameter_server.ir.public import (
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_get_varname_parts,
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)
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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 (
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var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH
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or var.desc.type() == core.VarDesc.VarType.FETCH_LIST
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or var.desc.type() == core.VarDesc.VarType.READER
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):
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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["pserver_endpoints"] = (
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self.role_maker._get_pserver_endpoints()
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)
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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.incubate.distributed.fleet.parameter_server.ir.public import (
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get_sparse_tablenames,
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)
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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["truncated_gaussian_random"] = [
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"seed",
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"mean",
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"std",
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]
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dist_varnames = get_sparse_tablenames(
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self.origin_main_program, True
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)
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sparse_varnames = get_sparse_tablenames(
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self.origin_main_program, False
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)
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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 embedding now, please use paddle.static.nn.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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]
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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 (
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op.type in opt_init_map.keys()
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and value_name == op.output("Out")[0]
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):
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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.incubate.distributed.fleet.parameter_server.distribute_transpiler.distributed_strategy import (
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GeoStrategy,
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SyncStrategy,
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)
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from paddle.incubate.distributed.fleet.parameter_server.ir.public import (
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_get_lr_ops,
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_has_global_step,
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)
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trainer_config = self.async_strategy.get_trainer_runtime_config()
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print(trainer_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 (
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self.role_maker._is_heter_parameter_server_mode
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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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)
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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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)
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from paddle.distributed.communicator import Communicator
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self._communicator = Communicator(
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trainer_config.mode, kwargs, trainer_config.get_communicator_flags()
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)
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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 = Executor(paddle.CPUPlace())
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if self.role_maker._is_heter_parameter_server_mode:
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heter_worker_device_guard = (
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self.context["valid_strategy"]
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.a_sync_configs["heter_worker_device_guard"]
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.upper()
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)
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if heter_worker_device_guard not in ["GPU", "XPU", "CPU"]:
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raise ValueError(
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f"Heter Worker Not Support Device {heter_worker_device_guard}"
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)
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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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paddle.CUDAPlace(
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int(os.getenv("FLAGS_selected_gpus", "0"))
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)
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)
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elif heter_worker_device_guard == "XPU":
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executor = Executor(
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paddle.XPUPlace(
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int(os.getenv("FLAGS_selected_xpus", "0"))
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)
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)
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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 (
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self.role_maker._is_heter_worker()
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and self.context["valid_strategy"].a_sync_configs["launch_barrier"]
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):
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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(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 += (
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self.compiled_strategy.get_optimize_varname_on_ps(var_name)
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)
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sparse_related_optimize_varnames = list(
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set(sparse_related_optimize_varnames)
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)
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distributed_varnames = self.compiled_strategy.get_sparse_varname_on_ps(
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True
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)
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distributed_related_optimize_varnames = []
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for var_name in distributed_varnames:
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distributed_related_optimize_varnames += (
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self.compiled_strategy.get_optimize_varname_on_ps(var_name)
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)
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distributed_related_optimize_varnames = list(
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set(distributed_related_optimize_varnames)
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)
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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
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+ distributed_varnames
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+ sparse_related_optimize_varnames
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+ distributed_related_optimize_varnames
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),
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default_main_program().list_vars(),
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)
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)
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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(f"There is no directory named '{model_dirname}'")
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# load dense
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paddle.static.load_vars(
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executor,
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main_program=default_main_program(),
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dirname=model_dirname,
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vars=remaining_vars,
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)
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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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)
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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=distributed_varnames
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+ distributed_related_optimize_varnames,
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)
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def _run_server(self):
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executor = self._get_executor()
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executor.run(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",
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"adam",
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"adagrad",
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"adamax",
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"momentum",
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"lars_momentum",
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"rmsprop",
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"decayed_adagrad",
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"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["rmsprop"] = [
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"momentum_0",
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"mean_square_0",
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"mean_grad_0",
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]
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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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f"fleet can not support optimizer: {op}, only this can be supported: {supported_opts}"
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)
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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.incubate.distributed.fleet.parameter_server.ir.public import (
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_get_optimize_ops,
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)
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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 (
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"Param" in op.input_names
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and "LearningRate" in op.input_names
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and op.input("Param")[0] == param_name
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):
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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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)
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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": [",".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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)
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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 = []
|
|
|
|
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(f"{reshaped_varname}.block{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(),
|
|
)
|
|
)
|
|
|
|
paddle.static.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 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_vars,
|
|
target_vars,
|
|
main_program=None,
|
|
export_for_deployment=True,
|
|
legacy_format=False,
|
|
):
|
|
"""
|
|
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 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"
|
|
)
|
|
paddle.static.io.save_inference_model(
|
|
dirname,
|
|
feeded_vars,
|
|
target_vars,
|
|
executor,
|
|
program=main_program,
|
|
legacy_format=legacy_format,
|
|
)
|
|
else:
|
|
paddle.static.save_inference_model(
|
|
dirname,
|
|
feeded_vars,
|
|
target_vars,
|
|
executor,
|
|
program=self.origin_main_program,
|
|
legacy_format=legacy_format,
|
|
)
|
|
|
|
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(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)
|