# Copyright (c) 2019 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 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 from paddle.fluid.optimizer import PipelineOptimizer as PO from .meta_optimizer_base import MetaOptimizerBase from .common import OpRole, OP_ROLE_KEY, OP_ROLE_VAR_KEY, CollectiveHelper, is_update_op, is_loss_grad_op, is_backward_op, is_optimizer_op class PipelineHelper(CollectiveHelper): def __init__(self, role_maker, nrings=1, wait_port='6174'): super(PipelineHelper, self).__init__(role_maker, nrings, wait_port) 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, 'device_id': OpRole.Forward }) def _broadcast_params(self): block = self.startup_program.global_block() ring_id = 0 for param in block.iter_parameters(): if param.is_distributed: continue 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}) class PipelineOptimizer(MetaOptimizerBase): def __init__(self, optimizer): super(PipelineOptimizer, self).__init__(optimizer) self.inner_opt = optimizer # we do not allow meta optimizer to be inner optimizer currently self.meta_optimizers_white_list = [] self.meta_optimizers_black_list = [] def _set_basic_info(self, loss, role_maker, user_defined_optimizer, user_defined_strategy): super(PipelineOptimizer, self)._set_basic_info( loss, role_maker, user_defined_optimizer, user_defined_strategy) num_microbatches = user_defined_strategy.pipeline_configs['micro_batch'] self.wrapped_opt = PO(self.inner_opt, num_microbatches=num_microbatches) def _can_apply(self): if not self.role_maker._is_collective: return False if self.user_defined_strategy.pipeline == True: return True return False def _disable_strategy(self, dist_strategy): dist_strategy.pipeline = False dist_strategy.pipeline_configs = {} def _enable_strategy(self, dist_strategy, context): # we do not support enable pipeline automatically right now return def minimize_impl(self, loss, startup_program=None, parameter_list=None, no_grad_set=None): optimize_ops, params_grads, prog_list = \ self.wrapped_opt.minimize(loss, startup_program, parameter_list, no_grad_set) if self.role_maker.worker_num() == 1: return optimize_ops, params_grads endpoints = self.role_maker.get_trainer_endpoints() current_endpoint = endpoints[self.role_maker.worker_index()] self.startup_program = startup_program if startup_program is None: self.startup_program = fluid.default_startup_program() assert prog_list self.main_program_list = prog_list self.main_program = loss.block.program nranks = len(endpoints) self.nranks = nranks self.nrings = len(self.main_program_list) self.rank = self.role_maker.worker_index() self.endpoints = endpoints self.current_endpoint = current_endpoint pipeline_helper = PipelineHelper(self.role_maker, nrings=self.nrings) pipeline_helper.update_startup_program(self.startup_program) self._transpile_main_program() return optimize_ops, params_grads def _transpile_main_program(self): self._insert_loss_grad_ops() for ring_id in range(self.nrings): self._insert_allreduce_ops(ring_id) def _insert_loss_grad_ops(self): """ In order to keep the learning rate consistent in different numbers of training workers, we scale the loss grad by the number of workers """ block = self.main_program_list[self.nrings - 1]['program'].global_block( ) for idx, op in reversed(list(enumerate(block.ops))): if is_loss_grad_op(op): loss_grad_var = block.vars[op.output_arg_names[0]] block._insert_op( idx + 1, type='scale', inputs={'X': loss_grad_var}, outputs={'Out': loss_grad_var}, attrs={ 'scale': 1.0 / self.nranks, OP_ROLE_KEY: OpRole.Backward }) def _insert_allreduce_ops(self, ring_id): block = self.main_program_list[ring_id]['program'].global_block() origin_block = self.main_program.global_block() grad = None for idx, op in reversed(list(enumerate(block.ops))): if is_backward_op(op) and \ OP_ROLE_VAR_KEY in op.attr_names: op_role_var = op.all_attrs()[OP_ROLE_VAR_KEY] if len(op_role_var) == 0: continue assert len(op_role_var) % 2 == 0 offset = idx for i in range(0, len(op_role_var), 2): param = block.vars[op_role_var[i]] grad = block.vars[op_role_var[i + 1]] origin_param = origin_block.vars[op_role_var[i]] if origin_param.is_distributed: continue if offset == idx: offset += 1 block._insert_op( offset, type='c_sync_calc_stream', inputs={'X': grad}, outputs={'Out': grad}, attrs={OP_ROLE_KEY: OpRole.Backward}) offset += 1 block._insert_op( offset, type='c_sync_calc_stream', inputs={'X': grad}, outputs={'Out': grad}, attrs={ 'ring_id': ring_id, OP_ROLE_KEY: OpRole.Backward }) if grad is None: return for idx, op in enumerate(block.ops): if is_optimizer_op(op): block._insert_op( idx + ring_id, type='c_sync_comm_stream', inputs={'X': grad}, outputs={'Out': grad}, attrs={'ring_id': ring_id, OP_ROLE_KEY: OpRole.Backward}) break