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| import torch | |
| import sys | |
| from torch import nn | |
| #from new_impl.cv.dnns.vit import make_softmax_prunable | |
| from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineClsFMModel, ElasticDNN_OfflineClsMDModel | |
| 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 clip import FM_to_MD_clip_Util | |
| from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util | |
| from clip import FMLoRA_clip_Util | |
| from new_impl.cv.elasticdnn.model.vit import ElasticViTUtil | |
| 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 | |
| # class ElasticDNN_ViT_OfflineClsFMModel(ElasticDNN_OfflineClsFMModel): | |
| # def generate_md_by_reducing_width(self, reducing_width_ratio, samples: torch.Tensor): | |
| # return FM_to_MD_ViT_Util().init_md_from_fm_by_reducing_width_with_perf_test(self.models_dict['main'], | |
| # reducing_width_ratio, samples).to(self.device) | |
| # def get_feature_hook(self) -> LayerActivation: | |
| # return LayerActivation(get_module(self.models_dict['main'], 'head'), True, self.device) | |
| # def get_elastic_dnn_util(self) -> ElasticDNNUtil: | |
| # return ElasticViTUtil() | |
| # def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
| # return F.cross_entropy(self.infer(x), y) | |
| # def get_lora_util(self) -> FMLoRA_Util: | |
| # return FMLoRA_ViT_Util() | |
| # def get_task_head_params(self): | |
| # head = get_module(self.models_dict['main'], 'head') | |
| # return list(head.parameters()) | |
| # class ElasticDNN_ViT_OfflineClsMDModel(ElasticDNN_OfflineClsMDModel): | |
| # def __init__(self, name: str, models_dict_path: str, device: str): | |
| # super().__init__(name, models_dict_path, device) | |
| # self.distill_criterion = CrossEntropyLossSoft() | |
| # def get_feature_hook(self) -> LayerActivation: | |
| # return LayerActivation(get_module(self.models_dict['main'], 'head'), True, self.device) | |
| # def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
| # return F.cross_entropy(self.infer(x), y) | |
| # def get_distill_loss(self, student_output, teacher_output): | |
| # return self.distill_criterion(student_output, teacher_output) | |
| # def get_matched_param_of_fm(self, self_param_name, fm: nn.Module): | |
| # # only between qkv.weight, norm.weight/bias | |
| # if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]): | |
| # return None | |
| # p = get_parameter(self.models_dict['main'], self_param_name) | |
| # if p.dim() == 0: | |
| # return None | |
| # elif p.dim() == 1 and 'norm' 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 'to_qkv.weight' in self_param_name: | |
| # ss = self_param_name.split('.') | |
| # fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv' | |
| # fm_qkv = get_module(fm, fm_qkv_name) | |
| # fm_abs_name = '.'.join(ss[0: -2]) + '.abs' | |
| # fm_abs = get_module(fm, fm_abs_name) | |
| # return torch.cat([ | |
| # fm_qkv.weight.data, # task-agnositc params | |
| # torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0) # 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: | |
| # # 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 get_matched_param_of_fm(self, self_param_name, fm: nn.Module): | |
| # # if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]): | |
| # # return None | |
| # # # 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz | |
| # # if 'to_qkv.weight' in self_param_name: | |
| # # ss = self_param_name.split('.') | |
| # # fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv' | |
| # # fm_qkv = get_module(fm, fm_qkv_name) | |
| # # fm_abs_name = '.'.join(ss[0: -2]) + '.abs' | |
| # # fm_abs = get_module(fm, fm_abs_name) | |
| # # return torch.cat([ | |
| # # fm_qkv.weight.data, # task-agnositc params | |
| # # torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0) # 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: | |
| # # fm_param_name = self_param_name.replace('.linear', '') | |
| # # return get_parameter(fm, fm_param_name) | |
| # # else: | |
| # # return get_parameter(fm, self_param_name) | |
| class ElasticDNN_beit_OfflineClsFMModel(ElasticDNN_OfflineClsFMModel): | |
| def generate_md_by_reducing_width(self, reducing_width_ratio, samples: torch.Tensor): | |
| return FM_to_MD_clip_Util().init_md_from_fm_by_reducing_width_with_perf_test(self.models_dict['main'], | |
| reducing_width_ratio, samples).to(self.device) | |
| def get_feature_hook(self) -> LayerActivation: | |
| return LayerActivation(get_module(self.models_dict['main'], 'classifier'), True, self.device) | |
| def get_elastic_dnn_util(self) -> ElasticDNNUtil: | |
| return ElasticclipUtil() | |
| def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
| return F.cross_entropy(self.infer(x), y) | |
| def get_lora_util(self) -> FMLoRA_Util: | |
| return FMLoRA_clip_Util() | |
| def get_task_head_params(self): | |
| head = get_module(self.models_dict['main'], 'classifier') | |
| return list(head.parameters()) | |
| class ElasticDNN_beit_OfflineClsMDModel(ElasticDNN_OfflineClsMDModel): | |
| def __init__(self, name: str, models_dict_path: str, device: str): | |
| super().__init__(name, models_dict_path, device) | |
| self.distill_criterion = CrossEntropyLossSoft() | |
| def get_feature_hook(self) -> LayerActivation: | |
| return LayerActivation(get_module(self.models_dict['main'], 'classifier'), True, self.device) | |
| def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
| return F.cross_entropy(self.infer(x), y) | |
| def get_distill_loss(self, student_output, teacher_output): | |
| return self.distill_criterion(student_output, teacher_output) | |
| def get_matched_param_of_fm(self, self_param_name, fm: nn.Module): | |
| # only between qkv.weight, norm.weight/bias | |
| if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]): | |
| return None | |
| p = get_parameter(self.models_dict['main'], 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) | |
| # 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz | |
| if ('attention.attention.query' in self_param_name or 'attention.attention.key' in self_param_name or \ | |
| 'attention.attention.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) | |
| return torch.cat([ | |
| fm_qkv.weight.data, # task-agnositc params | |
| torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0) # 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 ('attention.attention.query' in self_param_name or 'attention.attention.key' in self_param_name or \ | |
| 'attention.attention.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) | |
| else: | |
| return get_parameter(fm, self_param_name) | |
| #return None | |
| if __name__ == '__main__': | |
| from utils.dl.common.env import set_random_seed | |
| set_random_seed(1) | |
| # 1. init model | |
| #from dnns.vit import vit_b_16 | |
| fm_models_dict_path = 'new_impl/cv/clip/results/cls.py/20231113/999999-164026-/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/clip/cls.py/models/fm_best.pt' | |
| fm_models_dict = torch.load(fm_models_dict_path) | |
| #fm_models_dict['main'] = make_softmax_prunable(fm_models_dict['main']) | |
| fm_models_dict_path = save_models_dict_for_init(fm_models_dict, __file__, 'fm_clip_cls_lora') | |
| md_models_dict_path = save_models_dict_for_init({ | |
| 'main': -1 | |
| }, __file__, 'md_clip_cls_lora') | |
| torch.cuda.set_device(1) | |
| device = 'cuda' | |
| fm_model = ElasticDNN_beit_OfflineClsFMModel('fm', fm_models_dict_path, device) | |
| md_model = ElasticDNN_beit_OfflineClsMDModel('md', md_models_dict_path, device) | |
| # 2. init alg | |
| models = { | |
| 'fm': fm_model, | |
| 'md': md_model | |
| } | |
| fm_to_md_alg = ElasticDNN_MDPretrainingWoFBSAlg(models, get_res_save_dir(__file__, sys.argv[0])) | |
| # 3. init scenario | |
| scenario = build_scenario( | |
| source_datasets_name=['GTA5Cls', 'SuperviselyPersonCls'], | |
| target_datasets_order=['CityscapesCls', 'BaiduPersonCls'] * 15, | |
| da_mode='close_set', | |
| data_dirs={ | |
| 'GTA5Cls': '/data/zql/datasets/gta5_for_cls_task', | |
| 'SuperviselyPersonCls': '/data/zql/datasets/supervisely_person_for_cls_task', | |
| 'CityscapesCls': '/data/zql/datasets/cityscapes_for_cls_task', | |
| 'BaiduPersonCls': '/data/zql/datasets/baidu_person_for_cls_task' | |
| }, | |
| ) | |
| from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup | |
| fm_to_md_alg.run(scenario, hyps={ | |
| 'launch_tbboard': False, | |
| 'samples_size': (1, 3, 224, 224), | |
| 'generate_md_width_ratio': 8, | |
| 'train_batch_size': 256, | |
| 'val_batch_size': 512, | |
| 'num_workers': 16, | |
| 'optimizer': 'AdamW', | |
| 'optimizer_args': {'lr': 1e-4, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, | |
| 'scheduler': 'LambdaLR', | |
| 'scheduler_args': {'lr_lambda': get_linear_schedule_with_warmup(10000, 70000)}, | |
| 'num_iters': 80000, | |
| 'val_freq': 400, | |
| 'distill_loss_weight': 1.0 | |
| }) | |
| # TODO: | |
| # 1. train MD before inserting FBS? |