# 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 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_update_op, is_loss_grad_op, is_backward_op, is_optimizer_op class ModelParallelHelper(object): def __init__(self, role_maker, wait_port=True, megatron_dp=False): self.wait_port = wait_port self.role_maker = role_maker self.megatron_dp = megatron_dp def update_startup_program(self, startup_program=None, inner_parallelism=None): self.startup_program = startup_program nranks = self.role_maker._worker_num() rank = self.role_maker._worker_index() endpoints = self.role_maker._get_trainer_endpoints() current_endpoint = endpoints[rank] # Create ring 0 for all model parallel parts within a single model mp_endpoints = [] mp_rank = rank % inner_parallelism mp_id = rank // inner_parallelism for idx, ep in enumerate(endpoints): if idx // inner_parallelism == mp_id: mp_endpoints.append(ep) print("model parallel eps:{}, rank{}".format(mp_endpoints, mp_rank)) self._init_communicator(self.startup_program, current_endpoint, mp_endpoints, mp_rank, 0, self.wait_port) self._broadcast_params(0, broadcast_distributed_weight=False) print("megatron group size: {}".format(inner_parallelism)) print("megatron rank: {}".format(mp_rank)) print("megatron endpoints: {}".format(mp_endpoints)) if self.megatron_dp: mp_num = len(endpoints) // inner_parallelism if mp_num == 1: return # Create rings for gpus as the same model parallel part eps = [] dp_rank = rank // inner_parallelism dp_id = rank % inner_parallelism #if dp_rank == 1: dp_rank =0 #if dp_rank == 0: dp_rank =1 ring_id = 1 for idx, ep in enumerate(endpoints): if idx % inner_parallelism == dp_id: eps.append(ep) #ep = eps.pop(0) #eps.insert(1, ep) print("data parallel eps:{}, rank{}".format(eps, dp_rank)) self._init_communicator(self.startup_program, current_endpoint, eps, dp_rank, ring_id, self.wait_port) self._broadcast_params(ring_id, broadcast_distributed_weight=True) 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, ring_id, broadcast_distributed_weight): block = self.startup_program.global_block() for param in block.iter_parameters(): if not broadcast_distributed_weight and 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 }) 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 ModelParallelOptimizer(MetaOptimizerBase): def __init__(self, optimizer): super(ModelParallelOptimizer, self).__init__(optimizer) self.inner_opt = optimizer self.meta_optimizers_white_list = [ "RecomputeOptimizer", "AMPOptimizer", "LarsOptimizer", "LambOptimizer", ] self.meta_optimizers_black_list = ["GraphExecutionOptimizer", ] self.megatron_dp = False def _set_basic_info(self, loss, role_maker, user_defined_optimizer, user_defined_strategy): super(ModelParallelOptimizer, self)._set_basic_info( loss, role_maker, user_defined_optimizer, user_defined_strategy) self.inner_parallelism = user_defined_strategy.model_parallel_configs[ 'parallelism'] def _can_apply(self): if not self.role_maker._is_collective: return False if self.user_defined_strategy.model_parallel == True: return True return False def _disable_strategy(self, dist_strategy): dist_strategy.model_parallel = False dist_strategy.model_parallel_configs = {} def _enable_strategy(self, dist_strategy, context): dist_strategy.model_parallel = True dist_strategy.model_parallel_configs = {"parallelism": 1, } # the following function will be used by AMP if both Megatron and AMP are turn on together. def apply_gradients(self, params_grads): return self.minimize_impl(params_grads=params_grads) def minimize_impl(self, loss, startup_program=None, parameter_list=None, no_grad_set=None): 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() # (TODO) check the order of metaoptimizer # (TODO) check the params_grads optimize_ops, params_grads = self.inner_opt.minimize( loss, self.startup_program, parameter_list, no_grad_set) self.main_program = loss.block.program self.inner_parallelism = self.inner_parallelism self.nranks = len(endpoints) pipeline_helper = ModelParallelHelper(self.role_maker) pipeline_helper.update_startup_program(self.startup_program, self.inner_parallelism) assert self.nranks % self.inner_parallelism == 0 if self.megatron_dp: # data parallelism dp_parallelism = self.nranks // self.inner_parallelism self._transpile_main_program(loss, dp_parallelism) return optimize_ops, params_grads def _transpile_main_program(self, loss, dp_parallelism): self._insert_loss_grad_ops(loss, dp_parallelism) ring_id = 1 print("ring_id: ", ring_id) # for ring_id in range(1, dp_parallelism + 1): self._insert_allreduce_ops(loss, ring_id) def _insert_loss_grad_ops(self, loss, dp_parallelism): """ 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 = loss.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 / dp_parallelism, OP_ROLE_KEY: OpRole.Backward }) def _insert_allreduce_ops(self, loss, ring_id): block = loss.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]] #if 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_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 list(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