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oneshot.py
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oneshot.py
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import argparse
from transformers import BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
from transformers import BertConfig
import torch.nn.utils.prune as prune
import numpy as np
import torch
parser = argparse.ArgumentParser(description='PyTorch Cifar10 Training')
parser.add_argument('--weight', default='pre', type=str, help='file_dir')
parser.add_argument('--model', default='glue', type=str, help='file_dir')
parser.add_argument('--rate', default=0.2, type=float, help='rate')
args = parser.parse_args()
def pruning_model(model,px):
parameters_to_prune =[]
for ii in range(12):
parameters_to_prune.append((model.bert.encoder.layer[ii].attention.self.query, 'weight'))
parameters_to_prune.append((model.bert.encoder.layer[ii].attention.self.key, 'weight'))
parameters_to_prune.append((model.bert.encoder.layer[ii].attention.self.value, 'weight'))
parameters_to_prune.append((model.bert.encoder.layer[ii].attention.output.dense, 'weight'))
parameters_to_prune.append((model.bert.encoder.layer[ii].intermediate.dense, 'weight'))
parameters_to_prune.append((model.bert.encoder.layer[ii].output.dense, 'weight'))
parameters_to_prune.append((model.bert.pooler.dense, 'weight'))
parameters_to_prune = tuple(parameters_to_prune)
prune.global_unstructured(
parameters_to_prune,
pruning_method=prune.L1Unstructured,
amount=px,
)
def see_weight_rate(model):
sum_list = 0
zero_sum = 0
for ii in range(12):
sum_list = sum_list+float(model.bert.encoder.layer[ii].attention.self.query.weight.nelement())
zero_sum = zero_sum+float(torch.sum(model.bert.encoder.layer[ii].attention.self.query.weight == 0))
sum_list = sum_list+float(model.bert.encoder.layer[ii].attention.self.key.weight.nelement())
zero_sum = zero_sum+float(torch.sum(model.bert.encoder.layer[ii].attention.self.key.weight == 0))
sum_list = sum_list+float(model.bert.encoder.layer[ii].attention.self.value.weight.nelement())
zero_sum = zero_sum+float(torch.sum(model.bert.encoder.layer[ii].attention.self.value.weight == 0))
sum_list = sum_list+float(model.bert.encoder.layer[ii].attention.output.dense.weight.nelement())
zero_sum = zero_sum+float(torch.sum(model.bert.encoder.layer[ii].attention.output.dense.weight == 0))
sum_list = sum_list+float(model.bert.encoder.layer[ii].intermediate.dense.weight.nelement())
zero_sum = zero_sum+float(torch.sum(model.bert.encoder.layer[ii].intermediate.dense.weight == 0))
sum_list = sum_list+float(model.bert.encoder.layer[ii].output.dense.weight.nelement())
zero_sum = zero_sum+float(torch.sum(model.bert.encoder.layer[ii].output.dense.weight == 0))
sum_list = sum_list+float(model.bert.pooler.dense.weight.nelement())
zero_sum = zero_sum+float(torch.sum(model.bert.pooler.dense.weight == 0))
return 100*zero_sum/sum_list
config = BertConfig.from_pretrained(
'bert-base-uncased'
)
if args.model == 'glue':
if args.weight == 'rand':
print('random')
model = BertForSequenceClassification(config=config)
output = 'random_prun/'
elif args.weight == 'pre':
model = BertForSequenceClassification.from_pretrained(
'bert-base-uncased',
from_tf=bool(".ckpt" in 'bert-base-uncased'),
config=config
)
output = 'pretrain_prun/'
pruning_model(model, args.rate)
zero = see_weight_rate(model)
print('zero rate', zero)
mask_dict = {}
weight_dict = {}
model_dict = model.state_dict()
for key in model_dict.keys():
if 'mask' in key:
mask_dict[key] = model_dict[key]
else:
weight_dict[key] = model_dict[key]
torch.save(mask_dict, output+'mask.pt')
torch.save(weight_dict, output+'weight.pt')
elif args.model == 'squad':
if args.weight == 'rand':
print('random')
model = BertForQuestionAnswering(config=config)
output = 'random_prun/'
elif args.weight == 'pre':
model = BertForQuestionAnswering.from_pretrained(
'bert-base-uncased',
from_tf=bool(".ckpt" in 'bert-base-uncased'),
config=config
)
output = 'pretrain_prun/'
pruning_model(model, args.rate)
zero = see_weight_rate(model)
print('zero rate', zero)
mask_dict = {}
weight_dict = {}
model_dict = model.state_dict()
for key in model_dict.keys():
if 'mask' in key:
mask_dict[key] = model_dict[key]
else:
weight_dict[key] = model_dict[key]
torch.save(mask_dict, output+'mask.pt')
torch.save(weight_dict, output+'weight.pt')
elif args.model == 'pretrain':
if args.weight == 'rand':
print('random')
model = BertForMaskedLM(config=config)
output = 'random_prun/'
elif args.weight == 'pre':
model = BertForMaskedLM.from_pretrained(
'bert-base-uncased',
from_tf=bool(".ckpt" in 'bert-base-uncased'),
config=config
)
output = 'pretrain_prun/'
pruning_model(model, args.rate)
zero = see_weight_rate(model)
print('zero rate', zero)
mask_dict = {}
weight_dict = {}
model_dict = model.state_dict()
for key in model_dict.keys():
if 'mask' in key:
mask_dict[key] = model_dict[key]
else:
weight_dict[key] = model_dict[key]
torch.save(mask_dict, output+'mask.pt')
torch.save(weight_dict, output+'weight.pt')