115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
# -*- coding: utf-8 -*-
|
|
# Copyright (c) Alibaba, Inc. and its affiliates.
|
|
import copy
|
|
|
|
import torch.nn as nn
|
|
|
|
from scepter.modules.utils.config import dict_to_yaml
|
|
from scepter.modules.utils.distribute import gather_data, we
|
|
from scepter.modules.utils.probe import (ProbeData, merge_gathered_probe,
|
|
register_data)
|
|
|
|
|
|
class BaseModel(nn.Module):
|
|
para_dict = {
|
|
'PRETRAINED_MODEL': {
|
|
'value': None,
|
|
'description': 'Pretrained model path.'
|
|
}
|
|
}
|
|
|
|
def __init__(self, cfg, logger=None):
|
|
super(BaseModel, self).__init__()
|
|
self.logger = logger
|
|
self.cfg = cfg
|
|
self._probe_data = {}
|
|
self._dist_data = {}
|
|
|
|
def __repr__(self) -> str:
|
|
return f'{self.__class__.__name__}' + ' ' + super().__repr__()
|
|
|
|
def load_pretrained_model(self, pretrained_model):
|
|
pass
|
|
|
|
def register_probe(self, probe_data: dict):
|
|
probe_da, dist_da = register_data(probe_data,
|
|
key_prefix=__class__.__name__)
|
|
self._probe_data.update(probe_da)
|
|
for key in dist_da:
|
|
if key not in self._dist_data:
|
|
self._dist_data[key] = dist_da[key]
|
|
else:
|
|
for k, v in dist_da[key].items():
|
|
if k in self._dist_data[key]:
|
|
self._dist_data[key][k] += v
|
|
else:
|
|
self._dist_data[key][k] = v
|
|
|
|
def probe_data(self):
|
|
gather_probe_data = gather_data(self._probe_data)
|
|
_dist_data_list = gather_data([self._dist_data])
|
|
if not we.rank == 0:
|
|
self._probe_data = {}
|
|
self._dist_data = {}
|
|
# Iterate recurse the sub class's probe data for time-aware data.
|
|
for k, v in self._modules.items():
|
|
if isinstance(getattr(self, k), BaseModel):
|
|
for kk, vv in getattr(self, k).probe_data().items():
|
|
self._probe_data[f'{k}/{kk}'] = vv
|
|
|
|
if gather_probe_data is not None:
|
|
# Before processing, just merge the data.
|
|
self._probe_data = merge_gathered_probe(gather_probe_data)
|
|
reduce_dist_data = {}
|
|
if _dist_data_list is not None:
|
|
reduce_dist_data = {}
|
|
for one_data in _dist_data_list:
|
|
for k, v in one_data.items():
|
|
if k in reduce_dist_data:
|
|
for kk, vv in v.items():
|
|
if kk in reduce_dist_data[k]:
|
|
reduce_dist_data[k][kk] += vv
|
|
else:
|
|
reduce_dist_data[k][kk] = vv
|
|
else:
|
|
reduce_dist_data[k] = v
|
|
self._dist_data = reduce_dist_data
|
|
# Iterate recurse the sub class's probe data for reduce data.
|
|
self._probe_data[f'{__class__.__name__}_distribute'] = ProbeData(
|
|
self._dist_data)
|
|
norm_dist_data = {}
|
|
for key, value in self._dist_data.items():
|
|
total = 0
|
|
for k, v in value.items():
|
|
total += v
|
|
norm_v = {}
|
|
for k, v in value.items():
|
|
norm_v[k] = v / total
|
|
norm_dist_data[key] = norm_v
|
|
self._probe_data[f'{__class__.__name__}_norm_distribute'] = ProbeData(
|
|
norm_dist_data)
|
|
ret_data = copy.deepcopy(self._probe_data)
|
|
self._probe_data = {}
|
|
return ret_data
|
|
|
|
def clear_probe(self):
|
|
self._probe_data.clear()
|
|
|
|
@staticmethod
|
|
def get_config_template():
|
|
'''
|
|
{ "ENV" :
|
|
{ "description" : "",
|
|
"A" : {
|
|
"value": 1.0,
|
|
"description": ""
|
|
}
|
|
}
|
|
}
|
|
:return:
|
|
'''
|
|
return dict_to_yaml('MODELS',
|
|
__class__.__name__,
|
|
BaseModel.para_dict,
|
|
set_name=True)
|