# 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 from __future__ import division import os import paddle.fluid as fluid from paddle.fluid import core, unique_name from ..base.private_helper_function import wait_server_ready from .meta_optimizer_base import MetaOptimizerBase from .common import OpRole, OP_ROLE_KEY, OP_ROLE_VAR_KEY, CollectiveHelper, is_loss_grad_op, is_backward_op, is_optimizer_op class RawProgramOptimizer(MetaOptimizerBase): def __init__(self, optimizer): super(RawProgramOptimizer, self).__init__(optimizer) self.inner_opt = optimizer self.meta_optimizers_white_list = [ "RecomputeOptimizer", "AMPOptimizer", ] self.meta_optimizers_black_list = ["GraphExecutionOptimizer", ] self.global_ring_id = 0 def _set_basic_info(self, loss, role_maker, user_defined_optimizer, user_defined_strategy): super(RawProgramOptimizer, self)._set_basic_info( loss, role_maker, user_defined_optimizer, user_defined_strategy) self.without_graph_optimization = user_defined_strategy.without_graph_optimization def _can_apply(self): if not self.role_maker._is_collective: return False if self.without_graph_optimization == True: return True return False def _disable_strategy(self, dist_strategy): dist_strategy.without_graph_optimization = False def _enable_strategy(self, dist_strategy, context): dist_strategy.without_graph_optimization = True def _broadcast_params(self, ring_id): block = self.startup_program.global_block() param = None 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 }) if not param: return # no parameter on this device block.append_op( type='c_sync_comm_stream', inputs={'X': param}, outputs={'Out': param}, attrs={'ring_id': ring_id, OP_ROLE_KEY: OpRole.Forward}) def _get_process_group_info(self): # global ring info self.global_endpoints = self.endpoints self.global_rank = self.rank self.global_nranks = self.nranks def _init_process_group(self): self._get_process_group_info() collective_helper = CollectiveHelper(self.role_maker, wait_port=False) # Create global ring for all gpus (ring_id = 0) collective_helper._init_communicator( self.startup_program, self.current_endpoint, self.global_endpoints, self.global_rank, self.global_ring_id, True, self.global_ring_id, True) self._broadcast_params(self.global_ring_id) def minimize_impl(self, loss, startup_program=None, parameter_list=None, no_grad_set=None): self.endpoints = self.role_maker._get_trainer_endpoints() self.current_endpoint = self.endpoints[self.role_maker._worker_index()] self.rank = self.role_maker._worker_index() self.nranks = self.role_maker._worker_num() if startup_program is None: startup_program = fluid.default_startup_program() self.startup_program = startup_program block = loss.block program = block.program self.main_program = program optimize_ops, params_grads = self.inner_opt.minimize( loss, startup_program, parameter_list, no_grad_set) if self.nranks == 1: return optimize_ops, params_grads self._init_process_group() self.main_program = program if self.nranks > 1: self._transpile_main_program(loss) return optimize_ops, params_grads def _transpile_main_program(self, loss): self._insert_loss_grad_ops(loss) self._insert_allreduce_ops() def _insert_loss_grad_ops(self, loss): """ 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.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): block = self.main_program.global_block() ring_id = self.global_ring_id 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.attr(OP_ROLE_VAR_KEY) if len(op_role_var) == 0: continue assert len(op_role_var) % 2 == 0 offset = 1 for i in range(0, len(op_role_var), 2): param_name = op_role_var[i] param = block.var(param_name) grad_name = op_role_var[i + 1] grad = block.var(grad_name) if param.is_distributed: continue block._insert_op( idx + offset, type='c_sync_calc_stream', inputs={'X': grad}, outputs={'Out': grad}, attrs={OP_ROLE_KEY: OpRole.Backward, }) offset += 1 block._insert_op( idx + offset, type='c_allreduce_sum', 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, type='c_sync_comm_stream', inputs={'X': grad}, outputs={'Out': grad}, attrs={'ring_id': ring_id, OP_ROLE_KEY: OpRole.Backward}) break