File size: 4,001 Bytes
e841b45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | from torch import nn
import torch
from typing import Dict
task_config = {
"task1590_diplomacy_text_generation": "configs/SuperNI/task1590_diplomacy_text_generation",
"task181_outcome_extraction": "configs/SuperNI/task181_outcome_extraction",
"task591_sciq_answer_generation": "configs/SuperNI/task591_sciq_answer_generation",
"task1729_personachat_generate_next": "configs/SuperNI/task1729_personachat_generate_next",
"task1572_samsum_summary": "configs/SuperNI/task1572_samsum_summary",
"task1510_evalution_relation_extraction": "configs/SuperNI/task1510_evalution_relation_extraction",
"task748_glucose_reverse_cause_event_detection": "configs/SuperNI/task748_glucose_reverse_cause_event_detection",
"task002_quoref_answer_generation": "configs/SuperNI/task002_quoref_answer_generation",
"task1687_sentiment140_classification": "configs/SuperNI/task1687_sentiment140_classification",
"task511_reddit_tifu_long_text_summarization": "configs/SuperNI/task511_reddit_tifu_long_text_summarization",
"task875_emotion_classification": "configs/SuperNI/task511_reddit_tifu_long_text_summarization",
"task639_multi_woz_user_utterance_generation": "configs/SuperNI/task639_multi_woz_user_utterance_generation",
"task1290_xsum_summarization": "configs/SuperNI/task1290_xsum_summarization",
"task073_commonsenseqa_answer_generation": "configs/SuperNI/task073_commonsenseqa_answer_generation",
"task363_sst2_polarity_classification": "configs/SuperNI/task363_sst2_polarity_classification",
"dbpedia": "configs/Long_Sequence/dbpedia",
"amazon": "configs/Long_Sequence/amazon",
"agnews": "configs/Long_Sequence/agnews",
"yahoo": "configs/Long_Sequence/yahoo",
"yelp": "configs/Long_Sequence/yelp",
"copa": "configs/Long_Sequence/copa",
"mnli": "configs/Long_Sequence/mnli",
"cb": "configs/Long_Sequence/cb",
"imdb": "configs/Long_Sequence/imdb",
"multirc": "configs/Long_Sequence/multirc",
"sst2": "configs/Long_Sequence/sst2",
"boolq": "configs/Long_Sequence/boolq",
"rte": "configs/Long_Sequence/rte",
"wic": "configs/Long_Sequence/wic",
"qqp": "configs/Long_Sequence/qqp",
}
def lora_state_dict_A(model: nn.Module, bias: str = 'none', task_name=None) -> Dict[str, torch.Tensor]:
my_state_dict = model.state_dict()
if bias == 'none':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_A' in k}
elif bias == 'all':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_A' in k or 'bias' in k}
elif bias == 'lora_only':
to_return = {}
for k in my_state_dict:
if 'lora_' in k:
to_return[k] = my_state_dict[k]
bias_name = k.split('lora_A')[0]+'bias'
if bias_name in my_state_dict:
to_return[bias_name] = my_state_dict[bias_name]
return to_return
else:
raise NotImplementedError
def lora_state_dict_B(model: nn.Module, bias: str = 'none', task_name=None) -> Dict[str, torch.Tensor]:
my_state_dict = model.state_dict()
if bias == 'none':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_B' in k}
elif bias == 'all':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_B' in k or 'bias' in k}
elif bias == 'lora_only':
to_return = {}
for k in my_state_dict:
if 'lora_' in k:
to_return[k] = my_state_dict[k]
bias_name = k.split('lora_B')[0]+'bias'
if bias_name in my_state_dict:
to_return[bias_name] = my_state_dict[bias_name]
return to_return
else:
raise NotImplementedError
def lora_state_dict_s(model: nn.Module, bias: str = 'none', task_name=None) -> Dict[str, torch.Tensor]:
my_state_dict = model.state_dict()
if bias == 'none':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_s' in k}
else:
raise NotImplementedError |