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Paddle/python/paddle/fleet/meta_optimizers/common.py

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.fluid as fluid
from paddle.fluid import core, unique_name
from ..base.private_helper_function import wait_server_ready
OpRole = core.op_proto_and_checker_maker.OpRole
OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
OP_ROLE_VAR_KEY = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
def is_update_op(op):
return 'Param' in op.input_names and 'Grad' in op.input_names and \
"LearningRate" in op.input_names
def is_loss_grad_op(op):
if OP_ROLE_KEY not in op.attr_names:
return False
op_role = int(op.all_attrs()[OP_ROLE_KEY])
return op_role & int(OpRole.Backward) and op_role & int(OpRole.Loss)
def is_backward_op(op):
return OP_ROLE_KEY in op.attr_names and \
int(op.all_attrs()[OP_ROLE_KEY]) & int(OpRole.Backward)
def is_optimizer_op(op):
return OP_ROLE_KEY in op.attr_names and \
int(op.all_attrs()[OP_ROLE_KEY]) & int(OpRole.Optimize)
class CollectiveHelper(object):
def __init__(self, role_maker, nrings=1, wait_port='6174'):
self.nrings = nrings
self.wait_port = wait_port
self.role_maker = role_maker
def update_startup_program(self, startup_program=None):
self.startup_program = startup_program
if startup_program is None:
self.startup_program = fluid.default_startup_program()
endpoints = self.role_maker.get_trainer_endpoints()
current_endpoint = endpoints[self.role_maker.worker_index()]
for ring_id in range(self.nrings):
self._init_communicator(
self.startup_program, current_endpoint, endpoints,
self.role_maker.worker_index(), ring_id, self.wait_port)
self._broadcast_params()
def _init_communicator(self, program, current_endpoint, endpoints, rank,
ring_id, wait_port):
nranks = len(endpoints)
other_endpoints = endpoints[:]
other_endpoints.remove(current_endpoint)
if rank == 0 and wait_port:
wait_server_ready(other_endpoints)
block = program.global_block()
nccl_id_var = block.create_var(
name=unique_name.generate('nccl_id'),
persistable=True,
type=core.VarDesc.VarType.RAW)
block.append_op(
type='c_gen_nccl_id',
inputs={},
outputs={'Out': nccl_id_var},
attrs={
'rank': rank,
'endpoint': current_endpoint,
'other_endpoints': other_endpoints,
OP_ROLE_KEY: OpRole.Forward
})
block.append_op(
type='c_comm_init',
inputs={'X': nccl_id_var},
outputs={},
attrs={
'nranks': nranks,
'rank': rank,
'ring_id': ring_id,
OP_ROLE_KEY: OpRole.Forward
})
def _broadcast_params(self):
block = self.startup_program.global_block()
ring_id = -1
for param in block.iter_parameters():
if param.is_distributed:
continue
ring_id = (ring_id + 1) % self.nrings
block.append_op(
type='c_broadcast',
inputs={'X': param},
outputs={'Out': param},
attrs={
'ring_id': ring_id,
'root': 0,
OP_ROLE_KEY: OpRole.Forward
})
for ring_id in range(self.nrings):
block.append_op(
type='c_sync_comm_stream',
inputs={'X': param},
outputs={'Out': param},
attrs={'ring_id': ring_id,
OP_ROLE_KEY: OpRole.Forward})