from typing import List from data.dataloader import build_dataloader # from methods.elasticdnn.api.online_model import ElasticDNN_OnlineModel from new_impl.cv.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel import torch import sys from torch import nn from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel from new_impl.cv.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg from new_impl.cv.elasticdnn.model.base import ElasticDNNUtil from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_Util from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.vit import FMLoRA_ViT_Util from new_impl.cv.elasticdnn.model.vit import ElasticViTUtil 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 new_impl.cv.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg import tqdm from new_impl.cv.feat_align.mmd import mmd_rbf class ElasticDNN_ClsOnlineModel(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: x, y = x.to(self.device), 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 ElasticViTUtil() 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', 'cls_token', 'pos_embed']]): # return None # p = get_parameter(self.models_dict['md'], self_param_name) # if p.dim() == 0: # return None # elif p.dim() == 1 and 'norm' in self_param_name and 'weight' in self_param_name: # return get_parameter(fm, self_param_name) if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]): 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 'qkv.weight' in self_param_name: # ss = self_param_name.split('.') # fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv' # fm_qkv = get_module(fm, fm_qkv_name) # fm_abs_name = '.'.join(ss[0: -1]) + '.abs' # 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(torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0))) # 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 'mlp.fc1' 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 if ('attention.attention.projection_query' in self_param_name or 'attention.attention.projection_key' in self_param_name or \ 'attention.attention.projection_value' in self_param_name) and ('weight' in self_param_name): ss = self_param_name.split('.') fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv' fm_qkv = get_module(fm, fm_qkv_name) fm_abs_name = '.'.join(ss[0: -1]) + '.abs' fm_abs = get_module(fm, fm_abs_name) 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(torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0))) return torch.cat([ fm_qkv.weight.data, # task-agnositc params fm_abs._mul_lora_weight.data # task-specific params (LoRA) ], dim=0) elif ('attention.attention.projection_query' in self_param_name or 'attention.attention.projection_key' in self_param_name or \ 'attention.attention.projection_value' in self_param_name) and ('bias' in self_param_name): ss = self_param_name.split('.') fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv.bias' return get_parameter(fm, fm_qkv_name) elif 'intermediate.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 'output.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) 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 ('attention.attention.projection_query.weight' in md_param_name or 'attention.attention.projection_key.weight' in md_param_name or 'attention.attention.projection_value.weight' 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]) + '.qkv' 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]) + '.abs' 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): # 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', 'cls_token', 'pos_embed']]): return None p = get_parameter(self.models_dict['sd'], self_param_name) if p.dim() == 0: return None elif p.dim() == 1 and 'norm' 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 'qkv.weight' in self_param_name: return get_parameter(md, self_param_name) # 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 'mlp.fc1.0.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 'mlp.fc2' 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'], 'head') return list(head.parameters()) class ClsOnlineFeatAlignModel(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): return LayerActivation(get_module(self.models_dict['main'], 'classifier'), False, self.device) def forward_to_get_task_loss(self, x, y): return F.cross_entropy(self.infer(x), y) def get_mmd_loss(self, f1, f2): return mmd_rbf(f1, f2) def infer(self, x, *args, **kwargs): return self.models_dict['main'](x).logits 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: x, y = x.to(self.device), 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