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| from typing import List | |
| from data.dataloader import build_dataloader | |
| # from methods.elasticdnn.api.online_model import ElasticDNN_OnlineModel | |
| from methods.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel | |
| import torch | |
| import sys | |
| from torch import nn | |
| from methods.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel | |
| 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 methods.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_Util | |
| from methods.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util | |
| from methods.elasticdnn.pipeline.offline.fm_lora.vit import FMLoRA_ViT_Util | |
| from methods.elasticdnn.model.bert import ElasticBertUtil | |
| from utils.common.file import ensure_dir | |
| 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.dl.common.loss import CrossEntropyLossSoft | |
| import torch.nn.functional as F | |
| from utils.dl.common.env import create_tbwriter | |
| import os | |
| from utils.common.log import logger | |
| from utils.common.data_record import write_json | |
| # from methods.shot.shot import OnlineShotModel | |
| from methods.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg | |
| import tqdm | |
| from methods.feat_align.mmd import mmd_rbf | |
| class ElasticDNN_SeClsOnlineModel(ElasticDNN_OnlineModel): | |
| 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 ElasticBertUtil() | |
| 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']]): | |
| 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 'to_qkv.bias' in self_param_name: | |
| # ss = self_param_name.split('.') | |
| # fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv.bias' | |
| # return get_parameter(fm, fm_qkv_name) | |
| elif 'dense' in self_param_name and 'weight' in self_param_name: | |
| fm_param_name = self_param_name.replace('.linear', '') | |
| return get_parameter(fm, fm_param_name) | |
| # elif 'mlp.fc2' in self_param_name and 'weight' in self_param_name: | |
| # fm_param_name = self_param_name | |
| # return get_parameter(fm, fm_param_name) | |
| else: | |
| # return get_parameter(fm, self_param_name) | |
| return None | |
| 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) | |
| 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) | |
| # 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 'to_qkv.bias' in self_param_name: | |
| # ss = self_param_name.split('.') | |
| # fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv.bias' | |
| # return get_parameter(fm, fm_qkv_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 '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(fm, self_param_name) | |
| return None | |
| def get_task_head_params(self): | |
| head = get_module(self.models_dict['sd'], 'classifier') | |
| return list(head.parameters()) | |
| from methods.gem.gem_el_bert import OnlineGEMModel | |
| import tqdm | |
| from methods.feat_align.mmd import mmd_rbf | |
| from copy import deepcopy | |
| class SeClsOnlineGEMModel(OnlineGEMModel): | |
| def get_trained_params(self): | |
| qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'query' in n or 'key' in n or 'value' in n or 'dense' in n or 'LayerNorm' in n] | |
| return qkv_and_norm_params | |
| def forward_to_get_task_loss(self, x, y): | |
| return F.cross_entropy(self.infer(x), y) | |
| def add_cls_in_head(self, num_cls): | |
| return | |
| head: nn.Linear = get_module(self.models_dict['main'], 'head') | |
| new_head = nn.Linear(head.in_features, head.out_features + num_cls, head.bias is not None, device=self.device) | |
| 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['main'], 'head', new_head) | |
| 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 |