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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? |