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127 lines
4.3 KiB
127 lines
4.3 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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from __future__ import print_function
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import paddle.fluid as fluid
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from paddle.fluid import core, unique_name
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from ..base.private_helper_function import wait_server_ready
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OpRole = core.op_proto_and_checker_maker.OpRole
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OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
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OP_ROLE_VAR_KEY = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
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def is_update_op(op):
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return 'Param' in op.input_names and 'Grad' in op.input_names and \
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"LearningRate" in op.input_names
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def is_loss_grad_op(op):
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if OP_ROLE_KEY not in op.attr_names:
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return False
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op_role = int(op.all_attrs()[OP_ROLE_KEY])
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return op_role & int(OpRole.Backward) and op_role & int(OpRole.Loss)
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def is_backward_op(op):
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return OP_ROLE_KEY in op.attr_names and \
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int(op.all_attrs()[OP_ROLE_KEY]) & int(OpRole.Backward)
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def is_optimizer_op(op):
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return OP_ROLE_KEY in op.attr_names and \
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int(op.all_attrs()[OP_ROLE_KEY]) & int(OpRole.Optimize)
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class CollectiveHelper(object):
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def __init__(self, role_maker, nrings=1, wait_port='6174'):
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self.nrings = nrings
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self.wait_port = wait_port
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self.role_maker = role_maker
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def update_startup_program(self, startup_program=None):
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self.startup_program = startup_program
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if startup_program is None:
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self.startup_program = fluid.default_startup_program()
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endpoints = self.role_maker.get_trainer_endpoints()
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current_endpoint = endpoints[self.role_maker.worker_index()]
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for ring_id in range(self.nrings):
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self._init_communicator(
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self.startup_program, current_endpoint, endpoints,
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self.role_maker.worker_index(), ring_id, self.wait_port)
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self._broadcast_params()
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def _init_communicator(self, program, current_endpoint, endpoints, rank,
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ring_id, wait_port):
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nranks = len(endpoints)
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other_endpoints = endpoints[:]
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other_endpoints.remove(current_endpoint)
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if rank == 0 and wait_port:
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wait_server_ready(other_endpoints)
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block = program.global_block()
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nccl_id_var = block.create_var(
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name=unique_name.generate('nccl_id'),
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persistable=True,
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type=core.VarDesc.VarType.RAW)
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block.append_op(
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type='c_gen_nccl_id',
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inputs={},
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outputs={'Out': nccl_id_var},
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attrs={
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'rank': rank,
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'endpoint': current_endpoint,
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'other_endpoints': other_endpoints,
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OP_ROLE_KEY: OpRole.Forward
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})
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block.append_op(
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type='c_comm_init',
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inputs={'X': nccl_id_var},
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outputs={},
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attrs={
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'nranks': nranks,
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'rank': rank,
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'ring_id': ring_id,
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OP_ROLE_KEY: OpRole.Forward
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})
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def _broadcast_params(self):
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block = self.startup_program.global_block()
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ring_id = -1
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for param in block.iter_parameters():
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if param.is_distributed:
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continue
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ring_id = (ring_id + 1) % self.nrings
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block.append_op(
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type='c_broadcast',
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inputs={'X': param},
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outputs={'Out': param},
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attrs={
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'ring_id': ring_id,
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'root': 0,
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OP_ROLE_KEY: OpRole.Forward
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})
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for ring_id in range(self.nrings):
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block.append_op(
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type='c_sync_comm_stream',
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inputs={'X': param},
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outputs={'Out': param},
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attrs={'ring_id': ring_id,
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OP_ROLE_KEY: OpRole.Forward})
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