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 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_POSOnlineModel(ElasticDNN_OnlineModel): 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) # print(x) y = y.to(self.device) output = self.infer(x) # torch.Size([16, 512, 43]) torch.Size([16, 512]) for oi, yi, xi in zip(output, y, x['input_ids']): # oi: 512, 43; yi: 512 seq_len = xi.nonzero().size(0) # print(output.size(), y.size()) pred = F.softmax(oi, dim=-1).argmax(dim=-1) correct = torch.eq(pred[1: seq_len], yi[1: seq_len]).sum().item() # print(output.size(), y.size()) acc += correct sample_num += seq_len # pbar.set_description(f'seq_len: {seq_len}, cur_seq_acc: {(correct / seq_len):.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) # 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()) class POSOnlineFeatAlignModel(OnlineFeatAlignModel): 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 get_feature_hook(self): return LayerActivation(get_module(self.models_dict['main'], 'classifier'), False, self.device) def forward_to_get_task_loss(self, x, y): self.to_train_mode() o = self.infer(x) return F.cross_entropy(o.view(-1, o.size(-1)), y.view(-1)) def get_mmd_loss(self, f1, f2): # print(f1.size()) # return mmd_rbf(f1.mean(1).flatten(1), f2.mean(1).flatten(1)) return mmd_rbf(f1.flatten(1), f2.flatten(1)) def infer(self, x, *args, **kwargs): return self.models_dict['main'](**x) 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) # print(x) y = y.to(self.device) output = self.infer(x) # torch.Size([16, 512, 43]) torch.Size([16, 512]) for oi, yi, xi in zip(output, y, x['input_ids']): # oi: 512, 43; yi: 512 seq_len = xi.nonzero().size(0) # print(output.size(), y.size()) pred = F.softmax(oi, dim=-1).argmax(dim=-1) correct = torch.eq(pred[1: seq_len], yi[1: seq_len]).sum().item() # print(output.size(), y.size()) acc += correct sample_num += seq_len # pbar.set_description(f'seq_len: {seq_len}, cur_seq_acc: {(correct / seq_len):.4f}') acc /= sample_num return acc