new_impl/cv/beit/beit_fbs.py

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 beit import FM_to_MD_beit_Util
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util
from beit import FMLoRA_beit_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_beit_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 ElasticbeitUtil()
    
    def forward_to_get_task_loss(self, x, y, *args, **kwargs):
        return F.cross_entropy(self.infer(x).logits, y)
    
    def get_lora_util(self) -> FMLoRA_Util:
        return FMLoRA_beit_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).logits, 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/beit/results/beit_cls.py/20231028/999998-155807-/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/cv/beit/beit_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_beit_cls_lora')
    md_models_dict_path = save_models_dict_for_init({
        'main': -1
    }, __file__, 'md_beit_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': 4,
        
        'train_batch_size': 128,
        '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?