maker.py

#! /usr/bin/python3
src="KoichiYasuoka/gpt2-small-japanese-juman-upos"
tgt="KoichiYasuoka/gpt2-small-japanese-juman-ud-causal"
url="https://github.com/UniversalDependencies/UD_Japanese-GSDLUW"
import os
d=os.path.basename(url)
os.system("test -d "+d+" || git clone --depth=1 "+url)
os.system("for F in train dev test ; do cp "+d+"/*-$F.conllu $F.conllu ; done")
class UDCausalDataset(object):
  def __init__(self,conllu,tokenizer,embeddings=None):
    self.conllu=open(conllu,"r",encoding="utf-8")
    self.tokenizer=tokenizer
    self.embeddings=embeddings
    self.max_tokens=3
    self.seeks=[(0,0)]
    label=set(["SYM"])
    dep=set()
    s=self.conllu.readline()
    while s!="":
      if s=="\n":
        self.seeks.append((self.conllu.tell(),0))
      else:
        w=s.split("\t")
        if len(w)==10:
          if w[0].isdecimal():
            p=w[3] if w[5]=="_" else w[3]+"|"+w[5]
            label.add(p)
            dep.add(p+("|" if w[6]=="0" else "|l-" if int(w[0])<int(w[6]) else "|r-")+w[7])
            self.seeks.append((self.seeks[-1][0],int(w[0])))
            self.max_tokens=max(self.max_tokens,int(w[0])*2+1)
      s=self.conllu.readline()
    lid={}
    for i,l in enumerate(sorted(label)):
      lid[l],lid["B-"+l],lid["I-"+l]=i*3,i*3+1,i*3+2
    for i,d in enumerate(sorted(dep),len(lid)):
      lid[d]=i
    self.label2id=lid
  def __call__(*args):
    lid={l:i for i,l in enumerate(sorted(set(sum([list(t.label2id) for t in args],[]))))}
    for t in args:
      t.label2id=lid
    return lid
  def __del__(self):
    self.conllu.close()
  __len__=lambda self:len(self.seeks)-1
  def __getitem__(self,i):
    s,t=self.seeks[i]
    self.conllu.seek(s)
    form,upos,deps,w=[],[],[],[""]
    while w[0]!="\n":
      w=self.conllu.readline().split("\t")
      if len(w)==10:
        form.append(w[1])
        if w[0].isdecimal():
          upos.append(w[3] if w[5]=="_" else w[3]+"|"+w[5])
          deps.append((int(w[6]),w[7]))
    v=self.tokenizer(form,add_special_tokens=False)
    if t==0:
      i,u=[],[]
      for j,(x,y) in enumerate(zip(v["input_ids"],upos)):
        if x!=[]:
          i+=x
          u+=[y] if len(x)==1 else ["B-"+y]+["I-"+y]*(len(x)-1)
      emb=self.embeddings
      pad=self.tokenizer.pad_token_id
    else:
      import torch
      m=[]
      for x in v["input_ids"]:
        if x==[]:
          m.append(self.embeddings[self.tokenizer.unk_token_id,:])
        else:
          m.append(self.embeddings[x,:].sum(axis=0))
      m.append(self.embeddings[self.tokenizer.sep_token_id,:])
      m.append(self.embeddings[self.tokenizer.pad_token_id,:])
      emb=torch.stack(m)
      i,u=list(range(len(upos)+1)),upos+["SYM"]
      i.append(t-1)
      k,d=deps[t-1]
      u.append(upos[t-1]+"|"+d if k==0 else upos[t-1])
      for j in range(t,len(upos)):
        i.append(j)
        a,b=deps[j]
        u.append(upos[j]+"|r-"+b if a==t else upos[t-1]+"|l-"+d if j+1==k else upos[j])
      pad=-1
    j=self.max_tokens-len(i)
    if j>0:
      ids=i+[pad]*j
      upos=u+["SYM"]*j
    else:
      ids=i[0:self.max_tokens]
      upos=u[0:self.max_tokens]
    return {"inputs_embeds":emb[ids,:],"labels":[self.label2id[p] for p in upos]}
from transformers import ReformerTokenizer,AutoConfig,GPT2ForTokenClassification,DefaultDataCollator,TrainingArguments,Trainer
tkz=ReformerTokenizer.from_pretrained(src,use_fast=False)
trainDS=UDCausalDataset("train.conllu",tkz)
devDS=UDCausalDataset("dev.conllu",tkz)
testDS=UDCausalDataset("test.conllu",tkz)
lid=trainDS(devDS,testDS)
cfg=AutoConfig.from_pretrained(src,num_labels=len(lid),label2id=lid,id2label={i:l for l,i in lid.items()},ignore_mismatched_sizes=True)
mdl=GPT2ForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True)
trainDS.embeddings=mdl.get_input_embeddings().weight
trainDS.max_tokens=min(trainDS.max_tokens,cfg.max_position_embeddings)
arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=32,dataloader_pin_memory=False,output_dir=tgt,overwrite_output_dir=True,save_total_limit=2,learning_rate=5e-05,warmup_ratio=0.1,save_safetensors=False)
trn=Trainer(args=arg,data_collator=DefaultDataCollator(),model=mdl,train_dataset=trainDS)
trn.train()
trn.save_model(tgt)
tkz.save_pretrained(tgt)