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import torch | |
import sys | |
from torch import nn | |
from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineVQAFMModel, ElasticDNN_OfflineVQAMDModel | |
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 blip import FM_to_MD_blip_Util | |
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util | |
from blip import FMLoRA_blip_Util | |
from blip import ElasticblipUtil | |
from utils.dl.common.model import LayerActivation2, 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.common.log import logger | |
class ElasticDNN_blip_OfflineVQAFMModel(ElasticDNN_OfflineVQAFMModel): | |
def generate_md_by_reducing_width(self, reducing_width_ratio, samples: torch.Tensor): # TODO: | |
return FM_to_MD_blip_Util().init_md_from_fm_by_reducing_width_with_perf_test(self.models_dict['main'], | |
reducing_width_ratio, samples) | |
# raise NotImplementedError | |
def get_feature_hook(self) -> LayerActivation2: | |
return LayerActivation2(get_module(self.models_dict['main'], 'text_decoder.cls.predictions.decoder')) | |
def get_elastic_dnn_util(self) -> ElasticDNNUtil: # TODO: | |
return ElasticblipUtil() | |
def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
self.to_train_mode() | |
# print(x['input_ids'].size(), x['pixel_values'].size(), ) | |
#o = self.infer(x) | |
o = self.models_dict['main'](**y) | |
# print(o.size(), y.size(), o, y) | |
#return F.binary_cross_entropy_with_logits(o, y) * y.shape[1] | |
return o.loss | |
def get_lora_util(self) -> FMLoRA_Util: | |
return FMLoRA_blip_Util() | |
def get_task_head_params(self): | |
head = get_module(self.models_dict['main'], 'text_decoder.cls.predictions.decoder') | |
params_name = {k for k, v in head.named_parameters()} | |
logger.info(f'task head params: {params_name}') | |
return list(head.parameters()) | |
class ElasticDNN_blip_OfflineVQAMDModel(ElasticDNN_OfflineVQAMDModel): | |
# 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) -> LayerActivation2: | |
return LayerActivation2(get_module(self.models_dict['main'], 'text_decoder.cls.predictions.decoder')) | |
def forward_to_get_task_loss(self, x, y, *args, **kwargs): | |
self.to_train_mode() | |
# print(x['input_ids'].size(), x['pixel_values'].size(), ) | |
#o = self.infer(x) | |
o = self.models_dict['main'](**y) | |
# print(o.size(), y.size(), o, y) | |
#return F.binary_cross_entropy_with_logits(o, y) * y.shape[1] | |
return o.loss | |
def get_distill_loss(self, student_output, teacher_output): | |
# print(student_output, teacher_output) | |
return F.mse_loss(student_output, teacher_output.detach()) | |
def get_matched_param_of_fm(self, self_param_name, fm: nn.Module): # TODO: | |
if any([k in self_param_name for k in ['fbs', 'ab', 'embeddings']]): | |
return None | |
p = get_parameter(self.models_dict['main'], self_param_name) | |
if p.dim() == 0: | |
return None | |
elif p.dim() == 1 and 'LayerNorm' in self_param_name.lower() and 'weight' in self_param_name: | |
# if self_param_name.startswith('norm'): | |
# return None | |
return get_parameter(fm, self_param_name) | |
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.query.weight -> xx.query.fc.weight and xx.query.ab.0/1 | |
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('.') | |
# raise NotImplementedError() # TODO: | |
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) | |
return torch.cat([ | |
fm_qkv.weight.data, # task-agnositc params | |
fm_abs[1].weight @ fm_abs[0].weight | |
], dim=0) | |
elif ('query' in self_param_name or 'key' in self_param_name or \ | |
'value' in self_param_name) and ('bias' in self_param_name): | |
ss = self_param_name.split('.') | |
fm_qkv_name = '.'.join(ss[0: -1]) + '.fc.bias' | |
return get_parameter(fm, fm_qkv_name) | |
elif 'intermediate.dense' in self_param_name: | |
fm_param_name = self_param_name.replace('.linear', '') | |
return get_parameter(fm, fm_param_name) | |
elif 'qkv.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) | |
return torch.cat([ | |
fm_qkv.weight.data, # task-agnositc params | |
fm_abs[1].weight @ fm_abs[0].weight # 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 | |
res = get_parameter(fm, fm_param_name) | |
# print('mlp fc2 debug', fm_param_name, res is None) | |
return res | |
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) | |
# 3. init scenario | |
scenario = build_scenario( | |
source_datasets_name=['VQA_split1'], | |
target_datasets_order=['VQA_split1_c'] * 1, # TODO | |
da_mode='close_set', | |
data_dirs={ | |
'VQA_split1': '/data/zql/datasets/vqav2', | |
'VQA_split1_c': '/data/zql/datasets/vqav2' | |
}, | |
) | |
# 1. init model | |
# from dnns.deeplabv3.head import modify_forward_head | |
# modify_forward_head() # TODO: bring a bug | |
# from dnns.vit import vit_b_16 | |
fm_models_dict_path = 'new_impl/mm/Vis_bert/QuestionAnswering/results/blip_fbs.py/20231020/999999-162038-/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/mm/Vis_bert/QuestionAnswering/blip_fbs.py/models/fm_best.pt' | |
fm_models = torch.load(fm_models_dict_path) | |
fm_models_dict_path = save_models_dict_for_init(fm_models, __file__, 'fm_blip_vqa_lora') | |
md_models_dict_path = save_models_dict_for_init( | |
torch.load('new_impl/mm/Vis_bert/QuestionAnswering/results/blip_fbs.py/20231020/999999-162038-/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/mm/Vis_bert/QuestionAnswering/blip_fbs.py/models/md_best.pt'), | |
__file__, 'md_blip_vqa_raw_pretrained') | |
device = 'cuda' | |
fm_model = ElasticDNN_blip_OfflineVQAFMModel('fm', fm_models_dict_path, device) | |
md_model = ElasticDNN_blip_OfflineVQAMDModel('md', md_models_dict_path, device) | |
# 2. init alg | |
models = { | |
'fm': fm_model, | |
'md': md_model | |
} | |
from new_impl.cv.elasticdnn.api.algs.md_pretraining_index_v2_train_index_and_md import ElasticDNN_MDPretrainingIndexAlg | |
fm_to_md_alg = ElasticDNN_MDPretrainingIndexAlg(models, get_res_save_dir(__file__, sys.argv[0])) | |
sample_dataset = list(scenario.get_offline_datasets().values())[0]['train'] | |
sample = sample_dataset[0][0] | |
from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup | |
fm_to_md_alg.run(scenario, hyps={ | |
'launch_tbboard': False, | |
'samples_size': sample, | |
'FBS_r': 8, | |
'FBS_ignore_layers': [], | |
'train_batch_size': 16, | |
'val_batch_size': 256, | |
'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)}, | |
'indexes_optimizer_args': {'lr': 3e-3, 'momentum': 0.9, 'weight_decay': 5e-4}, | |
'num_iters': 80000, | |
'val_freq': 20, | |
'max_sparsity': 0.9, | |
'min_sparsity': 0.0, | |
'l1_reg_loss_weight': 1e-9, | |
'index_loss_weight': 1e-4, | |
'val_num_sparsities': 4, | |
'bn_cal_num_iters': 0, | |
'index_init': 'zero', | |
'index_guided_linear_comb_split_size': 512 | |
}) | |