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389 lines
13 KiB
389 lines
13 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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# limitations under the License.
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from __future__ import print_function
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import sys
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import math
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from functools import reduce
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import collections
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import six
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import logging
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import numpy as np
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from .. import core, unique_name
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from ..framework import Program, default_main_program, default_startup_program
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from .details import wait_server_ready
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__all__ = ['GradAllReduce', 'LocalSGD']
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OpRole = core.op_proto_and_checker_maker.OpRole
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class Collective(object):
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'''
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'''
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def __init__(self, nrings):
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self.nrings = nrings
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self.endpoints = None
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self.current_endpoint = None
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self.nranks = None
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self.rank = None
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self.startup_program = None
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self.main_program = None
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op_maker = core.op_proto_and_checker_maker
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self.op_role_key = op_maker.kOpRoleAttrName()
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self.op_role_var_key = op_maker.kOpRoleVarAttrName()
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def transpile(self, startup_program, main_program, rank, endpoints,
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current_endpoint, wait_port):
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# in case of '127.0.0.1:6700,127.0.0.1:6701,...'
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if isinstance(endpoints, str):
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endpoints = endpoints.split(',')
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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 = default_startup_program()
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self.main_program = main_program
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if main_program is None:
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self.main_program = default_main_program()
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self.nranks = len(endpoints)
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if self.nranks == 1 and self.mode != "single_process_multi_thread":
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raise ValueError('the number of endpoints must > 1')
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if rank < 0:
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raise ValueError('rank must >= 0')
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self.rank = rank
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if current_endpoint not in endpoints:
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raise ValueError('current endpoint %s is not in %s',
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current_endpoint, str(endpoints))
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self.endpoints = endpoints
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self.current_endpoint = current_endpoint
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self.wait_port = wait_port
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self.startup_program._origin_program = self.startup_program.clone()
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self._transpile_startup_program()
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self.main_program._origin_program = self.main_program.clone()
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self._transpile_main_program()
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def _transpile_main_program(self):
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raise NotImplementedError('call the inherited method of subclasses')
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def _transpile_startup_program(self):
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for ring_id in range(self.nrings):
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self._init_communicator(self.startup_program, self.current_endpoint,
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self.endpoints, self.rank, ring_id,
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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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self.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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self.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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self.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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self.op_role_key: OpRole.Forward})
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def _is_loss_grad_op(self, op):
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if self.op_role_key not in op.attr_names:
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return False
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op_role = int(op.all_attrs()[self.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(self, op):
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return self.op_role_key in op.attr_names and \
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int(op.all_attrs()[self.op_role_key]) & int(OpRole.Backward)
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def _is_update_op(self, 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_optimizer_op(self, op):
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return self.op_role_key in op.attr_names and \
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int(op.all_attrs()[self.op_role_key]) & int(OpRole.Optimize)
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class GradAllReduce(Collective):
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'''
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'''
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def __init__(self, nrings=2):
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Collective.__init__(self, nrings)
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self.mode = "grad_allreduce"
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def _transpile_main_program(self):
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self._insert_scale_loss_grad_ops()
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self._insert_allreduce_ops()
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def _insert_scale_loss_grad_ops(self):
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'''
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In order to keep the learning rate consistent in different numbers of
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training workers, we scale the loss grad by the number of workers
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'''
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block = self.main_program.global_block()
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for idx, op in reversed(list(enumerate(block.ops))):
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if self._is_loss_grad_op(op):
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loss_grad_var = block.vars[op.output_arg_names[0]]
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block._insert_op(
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idx + 1,
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type='scale',
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inputs={'X': loss_grad_var},
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outputs={'Out': loss_grad_var},
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attrs={
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'scale': 1.0 / self.nranks,
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self.op_role_key: OpRole.Backward
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})
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def _insert_allreduce_ops(self):
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block = self.main_program.global_block()
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ring_id = -1
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grad = None
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for idx, op in reversed(list(enumerate(block.ops))):
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if self._is_backward_op(op) and \
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self.op_role_var_key in op.attr_names:
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op_role_var = op.all_attrs()[self.op_role_var_key]
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if len(op_role_var) == 0:
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continue
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assert len(op_role_var) % 2 == 0
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offset = idx
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for i in range(0, len(op_role_var), 2):
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param = block.vars[op_role_var[i]]
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grad = block.vars[op_role_var[i + 1]]
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if param.is_distributed:
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continue
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if offset == idx:
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offset += 1
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block._insert_op(
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offset,
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type='c_sync_calc_stream',
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inputs={'X': grad},
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outputs={'Out': grad},
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attrs={self.op_role_key: OpRole.Backward})
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offset += 1
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# As we search ops reversedly, we should insert c_allreduce_sum
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# op in the same way to keep the ring_id alternate
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ring_id = (ring_id + 1) % self.nrings
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block._insert_op(
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offset,
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type='c_allreduce_sum',
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inputs={'X': grad},
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outputs={'Out': grad},
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attrs={
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'ring_id': ring_id,
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self.op_role_key: OpRole.Backward
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})
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if grad is None:
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return
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for idx, op in enumerate(block.ops):
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if self._is_optimizer_op(op):
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for ring_id in range(self.nrings):
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block._insert_op(
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idx + ring_id,
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type='c_sync_comm_stream',
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inputs={'X': grad},
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outputs={'Out': grad},
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attrs={
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'ring_id': ring_id,
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self.op_role_key: OpRole.Backward
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})
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break
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class LocalSGD(Collective):
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'''
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'''
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def __init__(self, nrings=2):
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Collective.__init__(self, nrings)
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self.snapshot_key = '@SNAPSHOT'
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self.mode = "local_sgd"
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def _transpile_startup_program(self):
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Collective._transpile_startup_program(self)
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block = self.startup_program.global_block()
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non_dist_params = []
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for param in block.iter_parameters():
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if not param.is_distributed:
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non_dist_params.append(param)
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for param in non_dist_params:
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snapshot = block.create_var(
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name=self.snapshot_name(param.name),
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shape=param.shape,
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persistable=True,
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stop_gradient=True)
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block.append_op(
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type='assign',
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inputs={'X': [param]},
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outputs={'Out': [snapshot]},
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attrs={self.op_role_key: OpRole.Forward})
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def snapshot_name(self, param_name):
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return param_name + self.snapshot_key
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def _transpile_main_program(self):
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block = self.main_program.global_block()
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ordered_param_snapshot = []
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ring_id = -1
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for idx, op in reversed(list(enumerate(block.ops))):
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if self._is_update_op(op):
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param = block.vars[op.input('Param')[0]]
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if param.is_distributed:
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continue
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snapshot = block.create_var(
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name=self.snapshot_name(param.name),
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shape=param.shape,
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persistable=True,
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stop_gradient=True,
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dtype=param.dtype)
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block._insert_op(
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idx + 1,
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type='elementwise_sub',
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inputs={'X': [snapshot],
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'Y': [param]},
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outputs={'Out': [param]},
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attrs={self.op_role_key: OpRole.Optimize})
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block._insert_op(
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idx + 2,
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type='c_sync_calc_stream',
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inputs={'X': param},
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outputs={'Out': param},
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attrs={self.op_role_key: OpRole.Optimize})
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ring_id = (ring_id + 1) % self.nrings
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block._insert_op(
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idx + 3,
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type='c_allreduce_sum',
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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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self.op_role_key: OpRole.Optimize
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})
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ordered_param_snapshot.append((param, snapshot))
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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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self.op_role_key: OpRole.Optimize})
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for param_snapshot in reversed(ordered_param_snapshot):
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param = param_snapshot[0]
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snapshot = param_snapshot[1]
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block.append_op(
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type='scale',
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inputs={'X': [param]},
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outputs={'Out': [param]},
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attrs={
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'scale': 1.0 / self.nranks,
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self.op_role_key: OpRole.Optimize
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})
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block.append_op(
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type='elementwise_sub',
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inputs={'X': [snapshot],
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'Y': [param]},
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outputs={'Out': [param]},
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attrs={self.op_role_key: OpRole.Optimize})
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block.append_op(
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type='assign',
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inputs={'X': [param]},
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outputs={'Out': [snapshot]},
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attrs={self.op_role_key: OpRole.Optimize})
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class SingleProcessMultiThread(GradAllReduce):
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'''
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'''
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def __init__(self):
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GradAllReduce.__init__(self, 1)
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self.mode = "single_process_multi_thread"
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def _transpile_startup_program(self):
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block = self.startup_program.global_block()
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block.append_op(type='c_comm_init_all', attrs={'ring_id': 0})
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