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195 lines
7.9 KiB
195 lines
7.9 KiB
# Copyright (c) 2019 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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import paddle
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from paddle.fluid.framework import core
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from paddle.fluid import compiler
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from .meta_optimizer_base import MetaOptimizerBase
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from ..base.private_helper_function import wait_server_ready
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def get_build_strategy(dist_strategy):
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build_strategy = paddle.BuildStrategy()
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build_strategy.enable_sequential_execution = \
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dist_strategy.sequential_execution
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build_strategy.remove_unnecessary_lock = True
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build_strategy.fuse_elewise_add_act_ops = \
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dist_strategy.fuse_elewise_add_act_ops
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build_strategy.fuse_bn_act_ops = \
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dist_strategy.fuse_bn_act_ops
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build_strategy.enable_auto_fusion = \
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dist_strategy.enable_auto_fusion
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build_strategy.fuse_relu_depthwise_conv = \
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dist_strategy.fuse_relu_depthwise_conv
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build_strategy.fuse_broadcast_ops = \
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dist_strategy.fuse_broadcast_ops
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build_strategy.sync_batch_norm = \
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dist_strategy.sync_batch_norm
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return build_strategy
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def get_execution_strategy(dist_strategy):
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execution_strategy = paddle.ExecutionStrategy()
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execution_strategy.num_threads = \
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dist_strategy.num_threads
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execution_strategy.num_iteration_per_drop_scope = \
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dist_strategy.num_iteration_per_drop_scope
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execution_strategy.num_iteration_per_run = \
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dist_strategy.num_iteration_per_run
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execution_strategy.use_thread_barrier = \
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dist_strategy.use_thread_barrier
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return execution_strategy
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class GraphExecutionOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super(GraphExecutionOptimizer, self).__init__(optimizer)
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self.inner_opt = optimizer
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# we do not allow meta optimizer to be inner optimizer currently
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self.meta_optimizers_white_list = []
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def _is_graph_out(self):
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return True
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def _can_apply(self):
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"""
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Basically, this is PE, and almost all programs can be executed here
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"""
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return True
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def backward(self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None,
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callbacks=None):
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pass
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# should fix the variable
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def _setup_nccl_op(self, startup_program, main_program):
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trainer_endpoints = self.role_maker.get_trainer_endpoints()
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trainers = trainer_endpoints
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trainer_id = self.role_maker.worker_index()
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current_endpoint = self.role_maker.get_trainer_endpoints()[trainer_id]
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trainer_endpoints_env = ",".join(trainer_endpoints)
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trainers_num = self.role_maker.worker_num()
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trainer_endpoints.remove(current_endpoint)
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if trainer_id == 0:
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wait_server_ready(trainer_endpoints)
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nccl_id_var = startup_program.global_block().create_var(
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name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW)
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for i in range(1, self.user_defined_strategy.nccl_comm_num):
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startup_program.global_block().create_var(
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name="NCCLID_{}".format(i),
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persistable=True,
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type=core.VarDesc.VarType.RAW)
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if self.user_defined_strategy.hierachical_allreduce:
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for i in range(0, self.user_defined_strategy.nccl_comm_num):
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startup_program.global_block().create_var(
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name="Hierarchical_inter_NCCLID_{}".format(i),
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persistable=True,
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type=core.VarDesc.VarType.RAW)
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startup_program.global_block().create_var(
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name="Hierarchical_exter_NCCLID_{}".format(i),
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persistable=True,
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type=core.VarDesc.VarType.RAW)
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startup_program.global_block().append_op(
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type="gen_nccl_id",
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inputs={},
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outputs={"NCCLID": nccl_id_var},
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attrs={
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"trainers": trainers,
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"trainer_id": trainer_id,
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"nccl_comm_num": self.user_defined_strategy.nccl_comm_num,
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"use_hierarchical_allreduce":
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self.user_defined_strategy.hierachical_allreduce,
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"hierarchical_allreduce_inter_ranks":
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self.user_defined_strategy.hierachical_allreduce_inter_ranks
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})
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def _try_to_compile(self, startup_program, main_program, loss):
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build_strategy = get_build_strategy(self.user_defined_strategy)
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exe_strategy = get_execution_strategy(self.user_defined_strategy)
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node_num = self.role_maker.worker_num()
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if self.role_maker._is_collective:
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assert node_num >= 1, "nccl2 node_num must >= 1, now:{}" % node_num
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if node_num <= 1:
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# local mode
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if self.user_defined_strategy.nccl_comm_num > 1:
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logging.warn("set nccl_comm_num=1 since you only have 1 node.")
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self.user_defined_strategy.nccl_comm_num = 1
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if self.user_defined_strategy.hierachical_allreduce:
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logging.warn(
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"set hierachical_allreduce=False since you only have 1 node."
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)
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self.user_defined_strategy.hierachical_allreduce = False
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sync_allreduce = self.user_defined_strategy.sync_nccl_allreduce
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if sync_allreduce:
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exe_strategy.num_threads = self.user_defined_strategy.nccl_comm_num + 1
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if self.user_defined_strategy.hierachical_allreduce:
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exe_strategy.num_threads = 2 * self.user_defined_strategy.nccl_comm_num + 1
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if exe_strategy.num_threads > 4:
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logging.warn(
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"if you use hierachical_allreduce or "
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"with multi nccl comm, please export FLAGS_sync_nccl_allreduce = 0"
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)
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# TODO(guru4elephant): should be an independent optimizer
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sync_batch_norm = self.user_defined_strategy.sync_batch_norm
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if sync_batch_norm:
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self.user_defined_strategy.nccl_comm_num = 1
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self.user_defined_strategy.hierachical_allreduce = False
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exe_strategy.num_threads = 1
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logging.warn(
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"use sync_batch_norm will hang when set num_threads > 1, so "
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"set num_threads=1, nccl_comm_num=1, hierachical_allreduce=False."
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)
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# TODO(guru4elephant): should be an independent optimizer
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self._setup_nccl_op(startup_program, main_program)
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build_strategy.num_trainers = self.role_maker.worker_num()
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build_strategy.trainer_id = self.role_maker.worker_index()
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build_strategy.trainers_endpoints = self.role_maker.get_trainer_endpoints(
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)
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build_strategy.enable_backward_optimizer_op_deps = True
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self._compiled_program = compiler.CompiledProgram(main_program)
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self._compiled_program.with_data_parallel(
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loss_name=loss.name,
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build_strategy=build_strategy,
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exec_strategy=exe_strategy,
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share_vars_from=None)
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return self._compiled_program
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def minimize(self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None):
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if startup_program == None:
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startup_program = paddle.default_startup_program()
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compiled_program = self._try_to_compile(startup_program,
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loss.block.program, loss)
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loss.block.program.graph = compiled_program
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# just return self.optimizer_ops and self.param_grads
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return None, None
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