import os #bert_path should be the path of the roberta-base dir os.environ['bert_path'] = '/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/nlp/roberta/sentiment-classification/roberta-base' os.environ["TOKENIZERS_PARALLELISM"] = "false" import torch import torch.nn as nn 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_lora.base import FMLoRA_Util from methods.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util from methods.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_Util from methods.elasticdnn.model.vit import ElasticViTUtil from methods.elasticdnn.api.algs.md_pretraining_index_v2_train_index_and_md import ElasticDNN_MDPretrainingIndexAlg from utils.dl.common.model import LayerActivation2, get_module, get_parameter from utils.common.exp import save_models_dict_for_init, get_res_save_dir from data import build_scenario import torch.nn.functional as F from utils.dl.common.loss import CrossEntropyLossSoft from new_impl.cv.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg import tqdm from new_impl.cv.feat_align.mmd import mmd_rbf from new_impl.cv.utils.baseline_da import baseline_da from methods.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel from utils.common.log import logger import json from roberta import FMLoRA_Roberta_Util, RobertaForSenCls, FM_to_MD_Roberta_Util, ElasticRobertaUtil from copy import deepcopy torch.cuda.set_device(1) # from methods.shot.shot import OnlineShotModel from experiments.utils.elasticfm_cl import init_online_model, elasticfm_cl # torch.multiprocessing.set_sharing_strategy('file_system') device = 'cuda:1' app_name = 'secls' scenario = build_scenario( source_datasets_name=['HL5Domains-ApexAD2600Progressive', 'HL5Domains-CanonG3', 'HL5Domains-CreativeLabsNomadJukeboxZenXtra40GB'], target_datasets_order=['HL5Domains-Nokia6610', 'HL5Domains-NikonCoolpix4300'] * 10, # TODO 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']} }, ) class SeClsOnlineFeatAlignModel(OnlineFeatAlignModel): def get_trained_params(self): # TODO: elastic fm only train a part of params #qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'attention.attention.projection_query' in n or 'attention.attention.projection_key' in n or 'attention.attention.projection_value' in n or 'intermediate.dense' in n or 'output.dense' in n] qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters()] return qkv_and_norm_params def get_feature_hook(self) -> LayerActivation2: return LayerActivation2(get_module(self.models_dict['main'], 'classifier')) def forward_to_get_task_loss(self, x, y): self.to_train_mode() return F.cross_entropy(self.infer(x), y) def get_mmd_loss(self, f1, f2): common_shape = min(f1.shape[0], f2.shape[0]) f1 = f1.view(f1.shape[0], -1) f2 = f2.view(f2.shape[0], -1) f1 = f1[:common_shape,:] f2 = f2[:common_shape,:] return mmd_rbf(f1, f2) def infer(self, x, *args, **kwargs): return self.models_dict['main'](**x) def get_accuracy(self, test_loader, *args, **kwargs): _d = test_loader.dataset from data import build_dataloader, split_dataset if _d.__class__.__name__ == '_SplitDataset' and _d.underlying_dataset.__class__.__name__ == 'MergedDataset': # necessary for CL print('\neval on merged datasets') merged_full_dataset = _d.underlying_dataset.datasets ratio = len(_d.keys) / len(_d.underlying_dataset) if int(len(_d) * ratio) == 0: ratio = 1. # print(ratio) # bs = # test_loaders = [build_dataloader(split_dataset(d, min(max(test_loader.batch_size, int(len(d) * ratio)), len(d)))[0], # TODO: this might be overlapped with train dataset # min(test_loader.batch_size, int(len(d) * ratio)), # test_loader.num_workers, False, None) for d in merged_full_dataset] test_loaders = [] for d in merged_full_dataset: n = int(len(d) * ratio) if n == 0: n = len(d) sub_dataset = split_dataset(d, min(max(test_loader.batch_size, n), len(d)))[0] loader = build_dataloader(sub_dataset, min(test_loader.batch_size, n), test_loader.num_workers, False, None) test_loaders += [loader] accs = [self.get_accuracy(loader) for loader in test_loaders] print(accs) return sum(accs) / len(accs) acc = 0 sample_num = 0 self.to_eval_mode() with torch.no_grad(): pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), dynamic_ncols=True, leave=False) for batch_index, (x, y) in pbar: for k, v in x.items(): if isinstance(v, torch.Tensor): x[k] = v.to(self.device) y = y.to(self.device) output = self.infer(x) pred = F.softmax(output, dim=1).argmax(dim=1) correct = torch.eq(pred, y).sum().item() acc += correct sample_num += len(y) # if batch_index == 0: # print(pred, y) pbar.set_description(f'cur_batch_total: {len(y)}, cur_batch_correct: {correct}, ' f'cur_batch_acc: {(correct / len(y)):.4f}') acc /= sample_num return acc da_alg = FeatAlignAlg from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup #from new_impl.cv.model import ClsOnlineFeatAlignModel da_model = SeClsOnlineFeatAlignModel( app_name, 'new_impl/nlp/roberta/sentiment-classification/results/cls_md_wo_fbs.py/20240113/999996-140353/models/md_best.pt', device ) da_alg_hyp = { 'HL5Domains-Nokia6610': { 'train_batch_size': 32, 'val_batch_size': 256, 'num_workers': 8, 'optimizer': 'AdamW', 'optimizer_args': {'lr': 2e-7, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, 'scheduler': '', 'scheduler_args': {}, 'num_iters': 100, 'val_freq': 20, 'feat_align_loss_weight': 1.0, }, 'HL5Domains-NikonCoolpix4300': { 'train_batch_size': 32, 'val_batch_size': 128, 'num_workers': 8, 'optimizer': 'AdamW', 'optimizer_args': {'lr': 2e-7, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, 'scheduler': '', 'scheduler_args': {}, 'num_iters': 100, 'val_freq': 20, 'feat_align_loss_weight': 1.0, }, } baseline_da( app_name, scenario, da_alg, da_alg_hyp, da_model, device, __file__, "results" )