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.elasticfm_da import init_online_model, elasticfm_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' sd_sparsity = 0.8 settings = { 'involve_fm': True } 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 ElasticDNN_SeClsOnlineModel(ElasticDNN_OnlineModel): @torch.no_grad() def sd_feedback_to_md(self, after_da_sd, unpruned_indexes_of_layers): self.models_dict['sd'] = after_da_sd self.before_da_md = deepcopy(self.models_dict['md']) logger.info('\n\nsurrogate DNN feedback to master DNN...\n\n') # one-to-one cur_unpruned_indexes = None cur_unpruned_indexes_name = None for p_name, p in self.models_dict['sd'].named_parameters(): matched_md_param = self.get_md_matched_param_of_sd_param(p_name) logger.debug(f'if feedback: {p_name}') if matched_md_param is None: continue logger.debug(f'start feedback: {p_name}, {p.size()} -> {matched_md_param.size()}') # average # setattr(matched_md_module, matched_md_param_name, (matched_md_param + p) / 2.) if p_name in unpruned_indexes_of_layers.keys(): cur_unpruned_indexes = unpruned_indexes_of_layers[p_name] cur_unpruned_indexes_name = p_name if p.size() != matched_md_param.size(): logger.debug(f'cur unpruned indexes: {cur_unpruned_indexes_name}, {cur_unpruned_indexes.size()}') if p.dim() == 1: # norm new_p = deepcopy(matched_md_param) new_p[cur_unpruned_indexes] = p elif p.dim() == 2: # linear if p.size(0) < matched_md_param.size(0): # output pruned new_p = deepcopy(matched_md_param) new_p[cur_unpruned_indexes] = p else: # input pruned new_p = deepcopy(matched_md_param) new_p[:, cur_unpruned_indexes] = p p = new_p assert p.size() == matched_md_param.size(), f'{p.size()}, {matched_md_param.size()}' # if 'classifier' in p_name: # continue # # if False: # # self.last_trained_cls_indexes # assert hasattr(self, 'last_trained_cls_indexes') # print(self.last_trained_cls_indexes) # diff = self._compute_diff(matched_md_param, p) # # matched_md_param[self.last_trained_cls_indexes].copy_(p[self.last_trained_cls_indexes.to(self.device)]) # matched_md_param.copy_(p) # logger.debug(f'SPECIFIC FOR CL HEAD | end feedback: {p_name}, diff: {diff:.6f}') # else: diff = self._compute_diff(matched_md_param, (matched_md_param + p) / 2.) matched_md_param.copy_((matched_md_param + p) / 2.) logger.debug(f'end feedback: {p_name}, diff: {diff:.6f}') def add_cls_in_head(self, num_cls): head: nn.Linear = get_module(self.models_dict['md'], 'classifier') new_head = nn.Linear(head.in_features, head.out_features + num_cls, head.bias is not None, device=self.device) # nn.init.zeros_(new_head.weight.data) # nn.init.zeros_(new_head.bias.data) new_head.weight.data[0: head.out_features] = deepcopy(head.weight.data) new_head.bias.data[0: head.out_features] = deepcopy(head.bias.data) set_module(self.models_dict['md'], 'classifier', new_head) set_module(self.models_dict['fm'], 'classifier', new_head) def get_accuracy(self, test_loader, *args, **kwargs): 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) 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 def get_elastic_dnn_util(self) -> ElasticDNNUtil: return ElasticRobertaUtil() def get_fm_matched_param_of_md_param(self, md_param_name): # only between qkv.weight, norm.weight/bias self_param_name = md_param_name fm = self.models_dict['fm'] if any([k in self_param_name for k in ['fbs', 'ab', 'embeddings','ln']]): return None p = get_parameter(self.models_dict['md'], self_param_name) if p.dim() == 0: return None elif p.dim() == 1 and 'LayerNorm' in self_param_name and 'weight' in self_param_name: return get_parameter(fm, self_param_name) # 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz if ('query' in self_param_name or 'key' in self_param_name or \ 'value' in self_param_name) and ('weight' in self_param_name): ss = self_param_name.split('.') fm_qkv_name = '.'.join(ss[0: -1]) + '.fc' fm_qkv = get_module(fm, fm_qkv_name) fm_abs_name = '.'.join(ss[0: -1]) + '.ab' fm_abs = get_module(fm, fm_abs_name) # NOTE: unrecoverable operation! multiply LoRA parameters to allow it being updated in update_fm_param() # TODO: if fm will be used for inference, _mul_lora_weight will not be applied! if not hasattr(fm_abs, '_mul_lora_weight'): logger.debug(f'set _mul_lora_weight in {fm_abs_name}') setattr(fm_abs, '_mul_lora_weight', nn.Parameter(fm_abs[1].weight @ fm_abs[0].weight)) return torch.cat([ fm_qkv.weight.data, # task-agnositc params fm_abs._mul_lora_weight.data # task-specific params (LoRA) ], dim=0) elif ('query' in self_param_name or 'key' in self_param_name or 'value' in self_param_name) \ and 'bias' in self_param_name: ss = self_param_name.split('.') fm_qkv_name = '.'.join(ss[0: -1]) + '.fc.bias' return get_parameter(fm, fm_qkv_name) elif 'intermediate.dense' in self_param_name: fm_param_name = self_param_name.replace('.linear', '') return get_parameter(fm, fm_param_name) else: return get_parameter(fm, self_param_name) def update_fm_param(self, md_param_name, cal_new_fm_param_by_md_param): if not ('query' in md_param_name or 'key' in md_param_name or 'value' in md_param_name): matched_fm_param_ref = self.get_fm_matched_param_of_md_param(md_param_name) matched_fm_param_ref.copy_(cal_new_fm_param_by_md_param) elif 'bias' in md_param_name: ss = md_param_name.split('.') fm = self.models_dict['fm'] fm_qkv_name = '.'.join(ss[0: -1]) + '.fc' fm_qkv = get_module(fm, fm_qkv_name) fm_qkv.bias.data.copy_(cal_new_fm_param_by_md_param) else: new_fm_attn_weight, new_fm_lora_weight = torch.chunk(cal_new_fm_param_by_md_param, 2, 0) ss = md_param_name.split('.') fm = self.models_dict['fm'] # update task-agnostic parameters fm_qkv_name = '.'.join(ss[0: -1]) + '.fc' fm_qkv = get_module(fm, fm_qkv_name) fm_qkv.weight.data.copy_(new_fm_attn_weight) # update task-specific parameters fm_abs_name = '.'.join(ss[0: -1]) + '.ab' fm_abs = get_module(fm, fm_abs_name) fm_abs._mul_lora_weight.data.copy_(new_fm_lora_weight) # TODO: this will not be applied in inference! def get_md_matched_param_of_fm_param(self, fm_param_name): return super().get_md_matched_param_of_fm_param(fm_param_name) def get_md_matched_param_of_sd_param(self, sd_param_name): # raise NotImplementedError # only between qkv.weight, norm.weight/bias self_param_name = sd_param_name md = self.models_dict['md'] if any([k in self_param_name for k in ['fbs', 'ab', 'embeddings']]): return None p = get_parameter(self.models_dict['sd'], self_param_name) if p.dim() == 0: return None elif p.dim() == 1 and 'LayerNorm' in self_param_name and 'weight' in self_param_name: return get_parameter(md, self_param_name) if 'classifier' in self_param_name: return get_parameter(md, self_param_name) elif 'static_channel_attention' in self_param_name: return None # 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz if ('query' in self_param_name or 'key' in self_param_name or \ 'value' in self_param_name) and ('weight' in self_param_name): return get_parameter(md, self_param_name) # NOTE: no fbs in qkv! elif ('query' in self_param_name or 'key' in self_param_name or \ 'value' in self_param_name) and ('bias' in self_param_name): return get_parameter(md, self_param_name) elif 'intermediate.dense.0.weight' in self_param_name: fm_param_name = '.'.join(self_param_name.split('.')[0: -2]) + '.linear.weight' return get_parameter(md, fm_param_name) elif 'intermediate.dense.0.bias' in self_param_name: fm_param_name = '.'.join(self_param_name.split('.')[0: -2]) + '.linear.bias' return get_parameter(md, fm_param_name) elif 'output.dense' in self_param_name and 'weight' in self_param_name: fm_param_name = self_param_name return get_parameter(md, fm_param_name) else: return get_parameter(md, self_param_name) def get_task_head_params(self): head = get_module(self.models_dict['sd'], 'classifier') return list(head.parameters()) 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 elasticfm_model = ElasticDNN_SeClsOnlineModel('secls', init_online_model( 'new_impl/nlp/roberta/sentiment-classification/results/cls_md_w_fbs_index.py/20240111/999998-203106-results/models/fm_best.pt', 'new_impl/nlp/roberta/sentiment-classification/results/cls_md_w_fbs_index.py/20240111/999998-203106-results/models/md_best.pt', 'cls', __file__ ), device, { 'md_to_fm_alpha': 0.01, 'fm_to_md_alpha': 0.1 }) 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 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, 'sd_sparsity':0.3, '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, 'sd_sparsity':0.3, 'feat_align_loss_weight': 1.0, }, } elasticfm_da( [app_name], [scenario], [elasticfm_model], [da_alg], [da_alg_hyp], [da_model], device, settings, __file__, "results", )