# Copyright (c) 2021 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 collections import numpy as np import paddle.fluid as fluid from paddle.fluid import core, unique_name from paddle.fluid.dygraph import Layer, LayerList 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 self.fuse_all_reduce_ops = user_defined_strategy.fuse_all_reduce_ops if self.fuse_all_reduce_ops: self.fuse_grad_size_in_num = user_defined_strategy.fuse_grad_size_in_num self.calc_comm_same_stream = user_defined_strategy._calc_comm_same_stream 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) if self.fuse_all_reduce_ops: self._allreduce_fusion_program() else: 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 # This function helps reduce the number of allreduce by integrating op, which can save communication time. # to use allreduce fuse, follow these codes: # strategy = paddle.distributed.fleet.DistributedStrategy() # strategy.without_graph_optimization = True # strategy.fuse_all_reduce_ops = True # strategy.calc_comm_same_stream = False # strategy.fuse_grad_size_in_num = 8 def _allreduce_fusion_program(self): block = self.main_program.global_block() ring_id = self.global_ring_id record_idx, allreduce_input_vars, allreduce_output_vars = [], [], [] ops = list(enumerate(block.ops)) for idx, op in reversed(ops): # we travers the ops reversely 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, "vars need to be one param var followed by one grad var, " \ "but got odd number of vars" for i in range(0, len(op_role_var), 2): # handle vars in each op, each time handle a param and a grad 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 if ".cast_fp16@GRAD" in grad_name: # when amp=True get the fp16 param param_name = param_name + ".cast_fp16" if not block.has_var(param_name): raise ValueError("op cast name error {}".format( op.type)) else: param = block.var(param_name) if len(allreduce_output_vars) == 0 or \ len(allreduce_output_vars[-1]) == \ self.fuse_grad_size_in_num: # start of the fusion or last group meets the config size allreduce_output_vars.append([grad]) allreduce_input_vars.append([param]) # add the start and end idx to the record idx record_idx.append([idx, idx]) else: # Current group's size is below the config size # append grad and param to the last group (current group) # update the start idx to current op's idx # Since we travers the ops reversely, the idx is descending # we update the first entry of each entry for record_idx allreduce_output_vars[-1].append(grad) allreduce_input_vars[-1].append(param) record_idx[-1][0] = idx assert len(allreduce_output_vars) == len( record_idx ), "It has different lens between the allreduce_output_vars and record_idx." if not allreduce_output_vars or not allreduce_input_vars: # nothing needs to be allreduced return self.vars = collections.OrderedDict() index, pos, offset = 0, 0, 0 start, end = record_idx[index] for idx, op in reversed(ops): if idx == start: pos = 0 done_output_vars, done_input_vars = self._split_fuction( allreduce_output_vars[index], # grad allreduce_input_vars[index] # param ) for id_, done_output_var in enumerate(done_output_vars): tmp_var = block.create_var( name=unique_name.generate('FusedOutput_{}'.format( done_output_var[0].name)), dtype=done_output_var[0].dtype, persistable=False, stop_gradient=True) self.vars['FusedOutput_{}'.format(done_output_var[0] .name)] = tmp_var block._insert_op( idx + id_, type="coalesce_tensor", inputs={"Input": done_input_vars[id_]}, outputs={ "Output": done_output_var, "FusedOutput": tmp_var }, attrs={ "copy_data": False, "use_align": True, "dtype": done_output_var[0].dtype, OP_ROLE_KEY: OpRole.Backward }) pos += 1 for id_ in range(len(done_output_vars)): x = self.vars['FusedOutput_{}'.format(done_output_vars[id_][ 0].name)] out = x # NOTE: there still some optimize space if use EVENT instead of sync if not self.calc_comm_same_stream: # need sync if the calc and comm stream are not the same block._insert_op( end + id_ + pos + 1, type='c_sync_calc_stream', inputs={'X': x}, outputs={'Out': out}, attrs={OP_ROLE_KEY: OpRole.Backward}) block._insert_op( end + id_ + pos + 1 if self.calc_comm_same_stream else end + id_ + pos + 2, type='c_allreduce_sum', inputs={'X': x}, outputs={'Out': out}, attrs={ 'ring_id': ring_id, 'use_calc_stream': self.calc_comm_same_stream, OP_ROLE_KEY: OpRole.Backward }) index += 1 if len(record_idx) == index: break start, end = record_idx[index] if not self.calc_comm_same_stream: # need sync if the calc and comm stream are not the same for idx, op in enumerate(block.ops): if is_optimizer_op(op): block._insert_op( idx, type='c_sync_comm_stream', inputs={'X': block.create_var()}, outputs={'Out': block.create_var()}, attrs={ 'ring_id': ring_id, OP_ROLE_KEY: OpRole.Backward }) break # Integrate grads of the same type to form a combination. # If combination is selected, will return grads of the same type in a groups. # For example:[(fp16, fp16), (fp32), (fp16)] -> [(fp16, fp16, fp16), (fp32)] def _split_fuction(self, allreduce_output_vars, allreduce_input_vars, combination=True): input_vars, final_input_vars, output_vars, final_output_vars = [], [], [], [] if len(allreduce_output_vars) == 1: # only have one var to handle final_output_vars.append(allreduce_output_vars) final_input_vars.append(allreduce_input_vars) return final_output_vars, final_input_vars for idx in range(len(allreduce_input_vars) - 1): # the last var needs to be handled differently if allreduce_input_vars[idx].dtype == allreduce_input_vars[idx + 1].dtype: # if current var and next var are in same type # append current var to input_vars input_vars.append(allreduce_input_vars[idx]) if idx == len(allreduce_input_vars) - 2: # if current var is the second last var # append the last var to input_vars # and update the final_input_vars input_vars.append(allreduce_input_vars[idx + 1]) final_input_vars.append(input_vars) else: # the current var and next var are in different types # append current var to input_vars # update the final_input_vars # reset input_vars to receive a new type input_vars.append(allreduce_input_vars[idx]) final_input_vars.append(input_vars) input_vars = [] if idx == len(allreduce_input_vars) - 2: # if current var is the second last var # append the last var to a reset input_vars since they are in different types # and update the final_input_vars input_vars.append(allreduce_input_vars[idx + 1]) final_input_vars.append(input_vars) for idx in range(len(allreduce_output_vars) - 1): # the procedure for the output vars is the same with that for the input vars if allreduce_output_vars[idx].dtype == allreduce_output_vars[ idx + 1].dtype: output_vars.append(allreduce_output_vars[idx]) if idx == len(allreduce_output_vars) - 2: output_vars.append(allreduce_output_vars[idx + 1]) final_output_vars.append(output_vars) else: output_vars.append(allreduce_output_vars[idx]) final_output_vars.append(output_vars) output_vars = [] if idx == len(allreduce_output_vars) - 2: output_vars.append(allreduce_output_vars[idx + 1]) final_output_vars.append(output_vars) # at this time, all vars in each group in final_input_vars and final_output_vars are in the same type if combination: input_fp16_vars, input_fp32_vars, output_fp16_vars, output_fp32_vars = [], [], [], [] for final_input_var in final_input_vars: if final_input_var[0].dtype == core.VarDesc.VarType.FP16: # extend the group input_fp16_vars.extend(final_input_var) else: input_fp32_vars.extend(final_input_var) for final_output_var in final_output_vars: if final_output_var[0].dtype == core.VarDesc.VarType.FP16: output_fp16_vars.extend(final_output_var) else: output_fp32_vars.extend(final_output_var) final_output_vars, final_input_vars = [], [] if output_fp16_vars: final_output_vars.append(output_fp16_vars) if output_fp32_vars: final_output_vars.append(output_fp32_vars) if input_fp16_vars: final_input_vars.append(input_fp16_vars) if input_fp32_vars: final_input_vars.append(input_fp32_vars) return final_output_vars, final_input_vars