|
import torch |
|
from methods.elasticdnn.api.model import ElasticDNN_OfflineSenClsFMModel, ElasticDNN_OfflineSenClsMDModel |
|
from methods.elasticdnn.api.algs.fm_lora import ElasticDNN_FMLoRAAlg |
|
from methods.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg |
|
from methods.elasticdnn.model.base import ElasticDNNUtil |
|
from methods.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util |
|
from bert import FMLoRA_Bert_Util |
|
from methods.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util |
|
from bert import FM_to_MD_Bert_Util |
|
from bert import ElasticBertUtil |
|
from utils.dl.common.model import LayerActivation, get_module, get_parameter, set_module |
|
from utils.common.exp import save_models_dict_for_init, get_res_save_dir |
|
from data import build_scenario |
|
from utils.common.log import logger |
|
import torch.nn.functional as F |
|
import sys |
|
|
|
class ElasticDNN_BERT_OfflineClsFMModel(ElasticDNN_OfflineSenClsFMModel): |
|
def generate_md_by_reducing_width(self, reducing_width_ratio, samples: torch.Tensor): |
|
return FM_to_MD_Bert_Util().init_md_from_fm_by_reducing_width_with_perf_test(self.models_dict['main'], |
|
reducing_width_ratio, samples) |
|
|
|
def get_feature_hook(self) -> LayerActivation: |
|
return LayerActivation(get_module(self.models_dict['main'], 'classifier'), True, self.device) |
|
|
|
def get_elastic_dnn_util(self) -> ElasticDNNUtil: |
|
return ElasticBertUtil() |
|
|
|
def forward_to_get_task_loss(self, x, y, *args, **kwargs): |
|
self.to_train_mode() |
|
pred = self.infer(x) |
|
|
|
return F.cross_entropy(pred, y) |
|
|
|
def get_lora_util(self) -> FMLoRA_Util: |
|
return FMLoRA_Bert_Util() |
|
|
|
def get_task_head_params(self): |
|
head = get_module(self.models_dict['main'], 'classifier') |
|
params_name = {k for k, v in head.named_parameters()} |
|
logger.info(f'task head params: {params_name}') |
|
return list(head.parameters()) |
|
|
|
class ElasticDNN_BERT_OfflineClsMDModel(ElasticDNN_OfflineSenClsMDModel): |
|
def get_feature_hook(self) -> LayerActivation: |
|
return LayerActivation(get_module(self.models_dict['main'], 'classifier'), True, self.device) |
|
|
|
def forward_to_get_task_loss(self, x, y, *args, **kwargs): |
|
self.to_train_mode() |
|
return self.models_dict['main'](x, y)['total_loss'] |
|
|
|
if __name__ == '__main__': |
|
from utils.dl.common.env import set_random_seed |
|
set_random_seed(1) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
scenario = build_scenario( |
|
source_datasets_name=['HL5Domains-ApexAD2600Progressive', 'HL5Domains-CanonG3', 'HL5Domains-CreativeLabsNomadJukeboxZenXtra40GB', |
|
'HL5Domains-NikonCoolpix4300'], |
|
target_datasets_order=['HL5Domains-Nokia6610'] * 1, |
|
da_mode='close_set', |
|
data_dirs={ |
|
**{k: f'/data/zql/datasets/nlp_asc_19_domains/dat/absa/Bing5Domains/asc/{k.split("-")[1]}' |
|
for k in ['HL5Domains-ApexAD2600Progressive', 'HL5Domains-CanonG3', 'HL5Domains-CreativeLabsNomadJukeboxZenXtra40GB', |
|
'HL5Domains-NikonCoolpix4300', 'HL5Domains-Nokia6610']} |
|
}, |
|
) |
|
|
|
|
|
device = 'cuda' |
|
from bert import bert_base_sen_cls |
|
cls_model = bert_base_sen_cls(num_classes=scenario.num_classes) |
|
|
|
|
|
|
|
|
|
|
|
fm_models_dict_path = save_models_dict_for_init({ |
|
'main': cls_model |
|
}, __file__, 'fm_bert_pretrained_with_cls_head') |
|
|
|
fm_model = ElasticDNN_BERT_OfflineClsFMModel('fm', fm_models_dict_path, device) |
|
|
|
models = { |
|
'fm': fm_model |
|
} |
|
fm_lora_alg = ElasticDNN_FMLoRAAlg(models, get_res_save_dir(__file__, 'result')) |
|
|
|
from PIL import ImageFile |
|
ImageFile.LOAD_TRUNCATED_IMAGES = True |
|
|
|
|
|
from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup |
|
fm_lora_alg.run(scenario, hyps={ |
|
'launch_tbboard': False, |
|
|
|
'samples_size': {'input_ids': torch.tensor([[ 101, 5672, 2033, 2011, 2151, 3793, 2017, 1005, 1040, 2066, 1012, 102]]).to(device), |
|
'token_type_ids': torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]).to(device), |
|
'attention_mask': torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]).to(device), 'return_dict': False}, |
|
|
|
'ab_r': 8, |
|
'train_batch_size': 8, |
|
'val_batch_size': 16, |
|
'num_workers': 16, |
|
'optimizer': 'AdamW', |
|
'optimizer_args': {'lr': 1e-4, 'betas': [0.9, 0.999]}, |
|
'scheduler': 'LambdaLR', |
|
'scheduler_args': {'lr_lambda': get_linear_schedule_with_warmup(10000, 310000)}, |
|
'num_iters': 50000, |
|
'val_freq': 400, |
|
|
|
|
|
}) |