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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 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
class ElasticDNN_blip_OfflineVQAFMModel(ElasticDNN_OfflineVQAFMModel):
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)
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)
o = self.models_dict['main'](**y)
# print(o.size(), y.size(), o, y)
#return F.cross_entropy(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 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()
o = self.infer(x)
return F.binary_cross_entropy_with_logits(o, y) * y.shape[1]
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'
},
)
# 2. init model
torch.cuda.set_device(1)
device = 'cuda'
from transformers import BlipForQuestionAnswering
from blip import blip
model = blip(scenario.num_classes)
fm_models_dict_path = save_models_dict_for_init({
'main': model
}, __file__, 'fm_blip')
fm_model = ElasticDNN_blip_OfflineVQAFMModel('fm', fm_models_dict_path, device)
# 3. init alg
models = {
'fm': fm_model
}
fm_lora_alg = ElasticDNN_FMLoRAAlg(models, get_res_save_dir(__file__, sys.argv[0]))
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)
# 4. run alg
from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup
fm_lora_alg.run(scenario, hyps={
'launch_tbboard': False,
'samples_size': sample,
'ab_r':8 ,
'train_batch_size': 64,
'val_batch_size': 512,
'num_workers': 16,
'optimizer': 'AdamW',
'optimizer_args': {'lr': 1e-4, 'betas': [0.9, 0.999]},
'scheduler': 'LambdaLR',
'scheduler_args': {'lr_lambda': get_linear_schedule_with_warmup(10000, 310000)},
'num_iters': 320000,
'val_freq': 400,
# 'fm_lora_ckpt_path': 'experiments/elasticdnn/vit_b_16/offline/fm_lora/cls/results/cls.py/20230607/999995-234355-TokenClsial/models/fm_best.pt'
}) |