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models.py
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import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
import os
from transformers import AutoTokenizer, AutoModel, AutoConfig, T5ForConditionalGeneration, BartForConditionalGeneration, AutoModelForSeq2SeqLM, RobertaConfig, RobertaModel, RobertaTokenizer
from transformers import PLBartForConditionalGeneration
import logging
import sys
# from GAT_prefix import CodeGraphPrefix
from utils import get_graph_metadata
from models_list.T5ForConditionalGeneration_Prefix import T5ForConditionalGeneration_Prefix
from models_list.T5ForConditionalGeneration_Prefix_2 import T5ForConditionalGeneration_Prefix_2
from models_list.PLBartForConditionalGeneration_Prefix import PLBartForConditionalGeneration_Prefix
from models_list.Seq2Seq import Seq2Seq, Seq2Seq4UniXcoder_completion, Seq2Seq4UniXcoder_generation, Seq2Seq4UniXcoder_e2d
from models_list.Classification_Model import Model4UniXcoder, CloneModel, DefectModel
from models_list.ClassificationModelforCalibration import CloneModelforCalibration,DefectModelforCalibration
from models_list.unified.prefixtuning import E2D_Model_Prefix
from models_list.unified.adaptertuning import E2D_Model_Adapter
from models_list.unified.bitfit import E2D_Model_Bitfit
# from models_list.prompt.modeling_t5 import T5ForConditionalGeneration
#import codecs
#sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach())
logger = logging.getLogger(__name__)
MODEL_CHECKPOINTS = {'roberta': 'roberta-base',
'codebert': 'microsoft/codebert-base',
'graphcodebert': 'microsoft/graphcodebert-base',
't5': 't5-base',
'codet5': 'Salesforce/codet5-base',
'bart': 'facebook/bart-base',
'plbart': 'uclanlp/plbart-base',
'unixcoder':'microsoft/unixcoder-base'}
MODEL_LOCALS = {
'roberta': 'roberta-base',
'codebert': 'codebert-base',
'graphcodebert': 'graphcodebert-base',
't5': 't5-base',
'codet5':'codet5-base',
'bart': 'bart-base',
'plbart': 'plbart-base',
'unixcoder':'unixcoder-base',
}
MODEL_CLASSES = {'roberta': (AutoConfig, AutoModel, AutoTokenizer),
'codebert': (AutoConfig, AutoModel, AutoTokenizer),
'graphcodebert': (AutoConfig, AutoModel, AutoTokenizer),
'unixcoder':(AutoConfig, AutoModel, AutoTokenizer),
't5': (AutoConfig, T5ForConditionalGeneration, AutoTokenizer),
'codet5': (AutoConfig, T5ForConditionalGeneration, AutoTokenizer),
'bart': (AutoConfig, BartForConditionalGeneration, AutoTokenizer),
'plbart':(AutoConfig, PLBartForConditionalGeneration, AutoTokenizer)}
# MODEL_CLASSES_PLG = {'roberta': (AutoConfig, AutoModel, AutoTokenizer),
# 'codebert': (AutoConfig, AutoModel, AutoTokenizer),
# 'graphcodebert': (AutoConfig, AutoModel, AutoTokenizer),
# 'unixcoder':(AutoConfig, AutoModel, AutoTokenizer),
# 't5': (AutoConfig, T5ForConditionalGeneration_Prefix, AutoTokenizer),
# 'codet5': (AutoConfig, T5ForConditionalGeneration_Prefix, AutoTokenizer),
# 'bart': (AutoConfig, BartForConditionalGeneration, AutoTokenizer),
# 'plbart':(AutoConfig, PLBartForConditionalGeneration_Prefix, AutoTokenizer)}
MODEL_CLASSES_PLG = {'roberta': (AutoConfig, AutoModel, AutoTokenizer),
'codebert': (AutoConfig, AutoModel, AutoTokenizer),
'graphcodebert': (AutoConfig, AutoModel, AutoTokenizer),
'unixcoder':(AutoConfig, AutoModel, AutoTokenizer),
't5': (AutoConfig, T5ForConditionalGeneration, AutoTokenizer),
'codet5': (AutoConfig, T5ForConditionalGeneration, AutoTokenizer),
'bart': (AutoConfig, BartForConditionalGeneration, AutoTokenizer),
'plbart':(AutoConfig, PLBartForConditionalGeneration, AutoTokenizer)}
MODEL_CLASSES_PLG_PREFIX = {'roberta': (AutoConfig, AutoModel, AutoTokenizer),
'codebert': (AutoConfig, AutoModel, AutoTokenizer),
'graphcodebert': (AutoConfig, AutoModel, AutoTokenizer),
'unixcoder':(AutoConfig, AutoModel, AutoTokenizer),
't5': (AutoConfig, T5ForConditionalGeneration_Prefix, AutoTokenizer),
'codet5': (AutoConfig, E2D_Model_Prefix, AutoTokenizer),
'bart': (AutoConfig, BartForConditionalGeneration, AutoTokenizer),
'plbart':(AutoConfig, E2D_Model_Prefix, AutoTokenizer)}
MODEL_CLASSES_PLG_ADAPTER = {'roberta': (AutoConfig, AutoModel, AutoTokenizer),
'codebert': (AutoConfig, AutoModel, AutoTokenizer),
'graphcodebert': (AutoConfig, AutoModel, AutoTokenizer),
'unixcoder':(AutoConfig, AutoModel, AutoTokenizer),
't5': (AutoConfig, T5ForConditionalGeneration_Prefix, AutoTokenizer),
'codet5': (AutoConfig, E2D_Model_Adapter, AutoTokenizer),
'bart': (AutoConfig, BartForConditionalGeneration, AutoTokenizer),
'plbart':(AutoConfig, E2D_Model_Adapter, AutoTokenizer)}
MODEL_CLASSES_PLG_BITFIT = {'roberta': (AutoConfig, AutoModel, AutoTokenizer),
'codebert': (AutoConfig, AutoModel, AutoTokenizer),
'graphcodebert': (AutoConfig, AutoModel, AutoTokenizer),
'unixcoder':(AutoConfig, AutoModel, AutoTokenizer),
't5': (AutoConfig, T5ForConditionalGeneration_Prefix, AutoTokenizer),
'codet5': (AutoConfig, E2D_Model_Bitfit, AutoTokenizer),
'bart': (AutoConfig, BartForConditionalGeneration, AutoTokenizer),
'plbart':(AutoConfig, E2D_Model_Bitfit, AutoTokenizer)}
# from transformers.models.roberta.modeling_roberta import RobertaModel
# from transformers.models.roberta.modeling_roberta import RobertaForMaskedLM
# from transformers.models.plbart.modeling_plbart import PLBartForConditionalGeneration
# MODEL_CLASSES = {'roberta': (AutoConfig, RobertaModel, AutoTokenizer),
# 'codebert': (AutoConfig, RobertaModel, AutoTokenizer),
# 'graphcodebert': (AutoConfig, RobertaForMaskedLM, AutoTokenizer),
# 'unixcoder':(AutoConfig, RobertaModel, AutoTokenizer),
# 't5': (AutoConfig, T5ForConditionalGeneration, AutoTokenizer),
# 'codet5': (AutoConfig, T5ForConditionalGeneration, AutoTokenizer),
# 'bart': (AutoConfig, BartForConditionalGeneration, AutoTokenizer),
# 'plbart':(AutoConfig, PLBartForConditionalGeneration, AutoTokenizer)}
# MODEL_CLASSES = {'roberta': (RobertaConfig, RobertaModel, RobertaTokenizer),
# 't5': (T5Config, T5ForConditionalGeneration, T5Tokenizer),
# 'codet5': (T5Config, T5ForConditionalGeneration, RobertaTokenizer),
# 'bart': (BartConfig, BartForConditionalGeneration, BartTokenizer)}
# from transformers import AutoModel
# model_name = "bert-base-cased"
# model = AutoModel.from_pretrained(model_name)
# print(model.config.model_type)
def get_model_size(model):
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
model_size = sum([np.prod(p.size()) for p in model_parameters])
return "{}M".format(round(model_size / 1e+6,3))
#如果addargument了prompt 在encoder的输出前就先加上prompt 用past_key_values 照着deltatuning加
#unixcoder seq2seq unilm 怎么实现三个mask
def bulid_or_load_gen_model(args):
# checkpoint = MODEL_CHECKPOINTS[args.model_name]
checkpoint = os.path.join(args.huggingface_locals, MODEL_LOCALS[args.model_name])
if args.prefix_tuning:
config_class, model_class, tokenizer_class = MODEL_CLASSES_PLG_PREFIX[args.model_name]
elif args.adapter_tuning:
config_class, model_class, tokenizer_class = MODEL_CLASSES_PLG_ADAPTER[args.model_name]
elif args.bitfit:
config_class, model_class, tokenizer_class = MODEL_CLASSES_PLG_BITFIT[args.model_name]
else:
config_class, model_class, tokenizer_class = MODEL_CLASSES_PLG[args.model_name]
config = config_class.from_pretrained(checkpoint)
tokenizer = tokenizer_class.from_pretrained(checkpoint)
print(config.model_type)
if args.model_name in ['roberta', 'codebert', 'graphcodebert']:
encoder = model_class.from_pretrained(checkpoint, output_attentions=True)
decoder_layer = nn.TransformerDecoderLayer(
d_model=config.hidden_size, nhead=config.num_attention_heads)
decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)
model = Seq2Seq(encoder=encoder, decoder=decoder, tokenizer=tokenizer, args=args,
config=config, beam_size=args.beam_size, max_length=args.max_target_length,
sos_id=tokenizer.cls_token_id, eos_id=tokenizer.sep_token_id)
elif args.model_name in ['unixcoder']:
# import！！！you must set is_decoder as True for generation in unixcoder！！！
config.is_decoder = True
encoder = model_class.from_pretrained(checkpoint, config=config)
if args.task in ['complete']:
if args.sub_task == "python":
eos_ids = [tokenizer.sep_token_id]
else:
eos_ids = [tokenizer.convert_tokens_to_ids('Ġ;'), tokenizer.convert_tokens_to_ids('Ġ}'), tokenizer.convert_tokens_to_ids('Ġ{')]
model=Seq2Seq4UniXcoder_completion(encoder=encoder,decoder=encoder,config=config, tokenizer=tokenizer, args=args,
beam_size=args.beam_size,max_length=args.max_target_length,
sos_id=tokenizer.cls_token_id,eos_id=eos_ids)
elif args.task in ['generate']:
model = Seq2Seq4UniXcoder_generation(encoder=encoder,decoder=encoder,config=config, tokenizer=tokenizer, args=args,
beam_size=args.beam_size,max_length=args.max_target_length,
sos_id=tokenizer.convert_tokens_to_ids(["<mask0>"])[0],eos_id=tokenizer.sep_token_id)
elif args.task in ['summarize','translate','refine']:
model = Seq2Seq4UniXcoder_e2d(encoder=encoder,decoder=encoder,config=config, tokenizer=tokenizer, args=args,
beam_size=args.beam_size,max_length=args.max_target_length,
sos_id=tokenizer.convert_tokens_to_ids(["<mask0>"])[0],eos_id=tokenizer.sep_token_id)
elif args.model_name in ['t5', 'codet5','bart','plbart']:
if hasattr(model_class,'from_pretrained'):
model = model_class.from_pretrained(checkpoint, output_attentions=True)#, args=args, tokenizer=tokenizer
else:# a wrapper model class
args.pretrained_model_name_or_path= checkpoint
model = model_class(args=args)
if args.prefix_tuning:
if hasattr(model,'code_prefix'):
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model.code_prefix.gat_layer), args.model_name)
elif hasattr(model,'knowledge_trans'):
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model.knowledge_trans), args.model_name)
elif hasattr(model.pretrain_model,'adapter'):
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model.pretrain_model.adapter), args.model_name)
else:
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model), args.model_name)
# if hasattr(model,'pretrain_model'):
# model = model.pretrain_model
# # print(hasattr(model,'parameters'))
elif args.adapter_tuning or args.bitfit:
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model), args.model_name)
else:
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model), args.model_name)
return config, model, tokenizer
def bulid_or_load_cls_model(args):
# checkpoint = MODEL_CHECKPOINTS[args.model_name]
checkpoint = os.path.join(args.huggingface_locals, MODEL_LOCALS[args.model_name])
if args.prefix_tuning:
# config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_name]
config_class, model_class, tokenizer_class = MODEL_CLASSES_PLG_PREFIX[args.model_name]
elif args.adapter_tuning:
config_class, model_class, tokenizer_class = MODEL_CLASSES_PLG_ADAPTER[args.model_name]
elif args.bitfit:
config_class, model_class, tokenizer_class = MODEL_CLASSES_PLG_BITFIT[args.model_name]
else:
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_name]
config = config_class.from_pretrained(checkpoint)
tokenizer = tokenizer_class.from_pretrained(checkpoint)
# if args.model_name in ['unixcoder']:
# model = model_class.from_pretrained(checkpoint, output_attentions=True)
# model = Model4UniXcoder(model,config,tokenizer,args)
if args.model_name not in ['t5', 'codet5','bart','plbart']:
model = model_class.from_pretrained(checkpoint, output_attentions=True)
else:
if hasattr(model_class,'from_pretrained'):
model = model_class.from_pretrained(checkpoint, output_attentions=True)
else:# a wrapper model class
args.pretrained_model_name_or_path= checkpoint
model = model_class(args=args)
# if not args.adapter_tuning and not args.bitfit:
if args.task == 'defect':
if args.calibrate_cls:
model = DefectModelforCalibration(model, config, tokenizer, args)
else:
model = DefectModel(model, config, tokenizer, args)
elif args.task == 'clone':
# model.resize_token_embeddings(32000)
if args.calibrate_cls:
model = CloneModelforCalibration(model, config, tokenizer, args)
else:
model = CloneModel(model, config, tokenizer, args)
if args.prefix_tuning:
if hasattr(model,'code_prefix'):
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model.code_prefix.gat_layer), args.model_name)
elif hasattr(model,'knowledge_trans'):
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model.knowledge_trans), args.model_name)
elif hasattr(model,'pretrain_model') and hasattr(model.pretrain_model,'adapter'):
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model.pretrain_model.adapter), args.model_name)
else:
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model), args.model_name)
else:
logger.info("Finish loading model [%s] parameters from %s", get_model_size(
model), args.model_name)
return config, model, tokenizer