wehub-resource-sync
320 lines
12 KiB
Python
Executable File
320 lines
12 KiB
Python
Executable File
# 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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import paddle
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from paddle.incubate.optimizer import PipelineOptimizer as PO
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from .common import (
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OP_ROLE_KEY,
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OP_ROLE_VAR_KEY,
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CollectiveHelper,
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OpRole,
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is_backward_op,
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is_loss_grad_op,
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)
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from .meta_optimizer_base import MetaOptimizerBase
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__all__ = []
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class PipelineOptimizer(MetaOptimizerBase):
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def __init__(self, optimizer):
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super().__init__(optimizer)
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self.inner_opt = optimizer
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self.meta_optimizers_white_list = [
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"RecomputeOptimizer",
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"AMPOptimizer",
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]
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self.meta_optimizers_black_list = []
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self.global_ring_id = 1
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self.dp_ring_id = 2
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self.start_pipeline_ring_id = 20 # Just a magic number
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def _set_basic_info(
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self, loss, role_maker, user_defined_optimizer, user_defined_strategy
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):
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super()._set_basic_info(
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loss, role_maker, user_defined_optimizer, user_defined_strategy
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)
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self.micro_batch_size = user_defined_strategy.pipeline_configs[
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'micro_batch_size'
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]
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self.num_microbatches = user_defined_strategy.pipeline_configs[
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'accumulate_steps'
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]
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self.schedule_mode = user_defined_strategy.pipeline_configs[
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'schedule_mode'
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]
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self.use_sharding = user_defined_strategy.sharding
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def _can_apply(self):
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if not self.role_maker._is_collective:
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return False
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# FIXME revise for hybrid parallelism
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if self.use_sharding:
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return False
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if self.user_defined_strategy.pipeline:
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return True
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return False
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def _disable_strategy(self, dist_strategy):
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dist_strategy.pipeline = False
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dist_strategy.pipeline_configs = {
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"micro_batch_size": 1,
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"accumulate_steps": 1,
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"schedule_mode": "1F1B",
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}
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def _enable_strategy(self, dist_strategy, context):
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dist_strategy.pipeline = True
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dist_strategy.pipeline_configs = {
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"micro_batch_size": 1,
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"accumulate_steps": 1,
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"schedule_mode": "1F1B",
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}
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def _broadcast_params(self, ring_id):
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block = self.startup_program.global_block()
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param = None
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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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block.append_op(
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type='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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OP_ROLE_KEY: OpRole.Forward,
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},
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)
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if not param:
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return # no parameter on this device
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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, OP_ROLE_KEY: OpRole.Forward},
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)
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def _get_process_group_info(self):
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# global ring info
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self.global_endpoints = self.endpoints
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self.global_rank = self.rank
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self.global_nranks = self.nranks
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# data parallel ring info
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if self.pipeline_num > 1:
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self.dp_rank = self.rank // self.inner_parallelism
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self.dp_nranks = self.nranks // self.inner_parallelism
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start_index = self.rank % self.inner_parallelism
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self.dp_endpoints = [
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self.endpoints[start_index + i * self.inner_parallelism]
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for i in range(self.pipeline_num)
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]
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def _init_process_group(self, pipeline_pair, pipeline_ring_map):
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self._get_process_group_info()
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collective_helper = CollectiveHelper(self.role_maker, wait_port=False)
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# Create global ring for all gpus (ring_id = 0)
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collective_helper._init_communicator(
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self.startup_program,
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self.current_endpoint,
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self.global_endpoints,
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self.global_rank,
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self.global_ring_id,
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True,
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self.global_ring_id,
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True,
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)
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# Create pipeline rings
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if self.inner_parallelism > 1:
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pipeline_id = self.rank // self.inner_parallelism
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start_index = pipeline_id * self.inner_parallelism
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for pair in pipeline_pair:
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pair_key = pair[0] * 1000 + pair[1]
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ring_id = pipeline_ring_map[pair_key]
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assert ring_id >= self.start_pipeline_ring_id
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first_node = pair[0] + start_index
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second_node = pair[1] + start_index
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if self.rank != first_node and self.rank != second_node:
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collective_helper._init_communicator(
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self.startup_program,
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None,
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None,
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None,
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None,
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False,
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self.global_ring_id,
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True,
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)
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continue
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pipeline_endpoints = [
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self.endpoints[first_node],
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self.endpoints[second_node],
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]
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pipeline_rank = 0 if self.rank == first_node else 1
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pipeline_nranks = 2
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collective_helper._init_communicator(
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self.startup_program,
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self.current_endpoint,
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pipeline_endpoints,
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pipeline_rank,
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ring_id,
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False,
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self.global_ring_id,
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True,
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)
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# Create dp rings
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if self.pipeline_num > 1:
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collective_helper._init_communicator(
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self.startup_program,
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self.current_endpoint,
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self.dp_endpoints,
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self.dp_rank,
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self.dp_ring_id,
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True,
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self.global_ring_id,
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True,
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)
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self._broadcast_params(self.dp_ring_id)
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def minimize_impl(
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self, loss, startup_program=None, parameter_list=None, no_grad_set=None
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):
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self.endpoints = self.role_maker._get_trainer_endpoints()
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self.current_endpoint = self.endpoints[self.role_maker._worker_index()]
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self.rank = self.role_maker._worker_index()
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self.nranks = self.role_maker._worker_num()
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self.wrapped_opt = PO(
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self.inner_opt, num_microbatches=self.num_microbatches
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)
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orig_startup_program = (
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startup_program
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if startup_program
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else paddle.static.default_startup_program()
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)
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block = loss.block
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program = block.program
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program._pipeline_opt = {}
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program._pipeline_opt['local_rank'] = self.rank
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program._pipeline_opt['global_ring_id'] = self.global_ring_id
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program._pipeline_opt['ring_id'] = self.start_pipeline_ring_id
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program._pipeline_opt['micro_batch_size'] = self.micro_batch_size
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program._pipeline_opt['schedule_mode'] = self.schedule_mode
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program._pipeline_opt['use_sharding'] = False
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program._pipeline_opt['mp_degree'] = 1
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program._pipeline_opt['mp_rank'] = 0
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(
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optimize_ops,
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params_grads,
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prog_list,
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pp_pair,
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ring_map,
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) = self.wrapped_opt.minimize(
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loss, startup_program, parameter_list, no_grad_set
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)
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self.startup_program = orig_startup_program._pipeline_opt[
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'startup_program'
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]
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self.inner_parallelism = program._pipeline_opt['inner_parallelism']
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assert self.nranks % self.inner_parallelism == 0
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assert prog_list
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self.pipeline_num = len(self.endpoints) // self.inner_parallelism
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self._init_process_group(pp_pair, ring_map)
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self.main_program_list = prog_list
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self.main_program = program
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if self.pipeline_num > 1:
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self._transpile_main_program(loss)
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return optimize_ops, params_grads
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def _transpile_main_program(self, loss):
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self._insert_loss_grad_ops(loss, self.pipeline_num)
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self._insert_allreduce_ops(self.dp_ring_id)
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def _insert_loss_grad_ops(self, loss, pipeline_num):
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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_list[-1].global_block()
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for idx, op in reversed(list(enumerate(block.ops))):
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if 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 / pipeline_num,
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OP_ROLE_KEY: OpRole.Backward,
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},
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)
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def _insert_allreduce_ops(self, ring_id):
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block = self.main_program._pipeline_opt[
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'section_program'
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].global_block()
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origin_block = self.main_program.global_block()
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grad = None
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processed_param_name = set()
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first_optimize_op_idx = None
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for idx, op in reversed(list(enumerate(block.ops))):
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if is_backward_op(op) and not first_optimize_op_idx:
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first_optimize_op_idx = idx + 1
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# no optimize phase
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if first_optimize_op_idx == len(block.ops):
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return
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if is_backward_op(op) and OP_ROLE_VAR_KEY in op.attr_names:
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op_role_var = op.all_attrs()[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 = 0
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for i in range(0, len(op_role_var), 2):
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param_name = op_role_var[i]
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param = block.vars[op_role_var[i]]
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if param_name in processed_param_name:
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continue
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processed_param_name.add(param_name)
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grad_name = op_role_var[i + 1]
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if 'MERGED' not in grad_name:
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grad_name += '@MERGED'
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grad = block.vars[grad_name]
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origin_param = origin_block.vars[op_role_var[i]]
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if origin_param.is_distributed:
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continue
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block._insert_op(
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first_optimize_op_idx + offset,
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type='all_reduce',
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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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'reduce_type': paddle.distributed.ReduceOp.SUM,
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OP_ROLE_KEY: OpRole.Optimize,
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},
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)
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