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import torch
from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineVQAFMModel, ElasticDNN_OfflineVQAMDModel
from new_impl.cv.elasticdnn.api.algs.fm_lora import ElasticDNN_FMLoRAAlg
from new_impl.cv.elasticdnn.model.base import ElasticDNNUtil
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util
from blip import FMLoRA_blip_Util
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 utils.dl.common.model import LayerActivation, get_module, get_parameter, set_module
from utils.common.exp import save_models_dict_for_init, get_res_save_dir
from data import build_scenario
from utils.common.log import logger
import torch.nn.functional as F
import sys
from torch import nn
from new_impl.cv.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg
class ElasticDNN_blip_OfflineVQAFMModel(ElasticDNN_OfflineVQAFMModel):
def generate_md_by_reducing_width(self, reducing_width_ratio, samples: torch.Tensor):
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) -> LayerActivation:
return LayerActivation(get_module(self.models_dict['main'], 'text_decoder.cls.predictions.decoder'), True, self.device)
def get_elastic_dnn_util(self) -> ElasticDNNUtil:
raise NotImplementedError
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).logits
# print(o.size(), y.size(), o, y)
return F.binary_cross_entropy_with_logits(o, y) * y.shape[1]
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 get_feature_hook(self) -> LayerActivation:
return LayerActivation(get_module(self.models_dict['main'], 'text_decoder.cls.predictions.decoder'), True, self.device)
# 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)
# # print(o.size(), y.size(), o, y)
# return F.binary_cross_entropy_with_logits(o, y) * y.shape[1]
def forward_to_get_task_loss(self, x, y, *args, **kwargs):
self.to_train_mode()
o = self.models_dict['main'](**y)
return o.loss
def get_distill_loss(self, student_output, teacher_output):
#print(student_output.shape, teacher_output.shape)
return F.mse_loss(student_output, teacher_output.detach())
#return F.cross_entropy(student_output, teacher_output.detach())
def get_trained_params(self):
return self.models_dict['main'].parameters()
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 and 'weight' in self_param_name:
# if self_param_name.startswith('norm'):
# return None
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)
else:
return get_parameter(fm, self_param_name)
if __name__ == '__main__':
from utils.dl.common.env import set_random_seed
set_random_seed(1)
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
fm_models_dict_path = 'new_impl/mm/Vis_bert/QuestionAnswering/results/blip_lora.py/20231018/999999-095006-/data/zql/concept-drift-in-edge-projects/UniversalElasticNet/new_impl/mm/Vis_bert/QuestionAnswering/blip_lora.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({
'main': -1
}, __file__, 'md_blip_none')
torch.cuda.set_device(1)
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
}
import sys
# from experiments.elasticdnn.clip.offline.fm_to_md.cls.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg
# from methods.elasticdnn.api.algs.md_pretraining_wo_fbs_clip_debug import ElasticDNN_MDPretrainingWoFBSAlg
fm_to_md_alg = ElasticDNN_MDPretrainingWoFBSAlg(models, get_res_save_dir(__file__, sys.argv[0]))
# sample_dataset = list(scenario.get_offline_datasets().values())[0]['train']
sample_dataset = list(scenario.get_offline_datasets().values())[0]['train']
sample = sample_dataset[0][0]
for k, v in sample.items():
sample[k] = v.unsqueeze(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,
'generate_md_width_ratio': 4,
'train_batch_size':32,
'val_batch_size': 512,
'num_workers': 16,
'optimizer': 'AdamW',
'optimizer_args': {'lr': 1e-5, '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': 1000,
'distill_loss_weight': 1.
})
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