|
#! /bin/sh |
|
test -f ja_gsd_modern.conllu || curl -LO https://github.com/KoichiYasuoka/SuPar-UniDic/raw/main/suparunidic/suparmodels/ja_gsd_modern.conllu |
|
test -f JapaneseCoreKanji.txt || curl -LO https://www.unicode.org/wg2/iso10646/edition6/data/JapaneseCoreKanji.txt |
|
|
|
if [ ! -d exSwallow-MS-7b-v0.1 ] |
|
then TMPA=./maker$$a.py |
|
cat << 'EOF' > $TMPA |
|
|
|
src="tokyotech-llm/Swallow-MS-7b-v0.1" |
|
tgt="exSwallow-MS-7b-v0.1" |
|
import json,torch,unicodedata |
|
from transformers import LlamaTokenizerFast,MistralForCausalLM |
|
with open("JapaneseCoreKanji.txt","r",encoding="utf-8") as r: |
|
cjk=[chr(int(t,16)) for t in r.read().strip().split("\n") if not t.startswith("#")] |
|
with open("ja_gsd_modern.conllu","r",encoding="utf-8") as r: |
|
for s in r: |
|
t=s.split("\t") |
|
if len(t)==10: |
|
for c in t[1]: |
|
if unicodedata.name(c).startswith("CJK "): |
|
cjk.append(c) |
|
cjk=list(set(cjk)) |
|
tkz=LlamaTokenizerFast.from_pretrained(src,cls_token="<s>",sep_token="<s>",mask_token="<unk>",pad_token="</s>") |
|
c={i:j[2:] for i,j in zip(cjk,tkz(cjk)["input_ids"]) if len(j)>3} |
|
d=json.loads(tkz.backend_tokenizer.to_str()) |
|
for i,j in enumerate(c,len(tkz)): |
|
d["model"]["vocab"][j]=i |
|
tkz.backend_tokenizer.from_str(json.dumps(d)).save("tokenizer.json") |
|
mdl=MistralForCausalLM.from_pretrained(src) |
|
tkz=LlamaTokenizerFast(tokenizer_file="tokenizer.json",model_max_length=mdl.config.max_position_embeddings,cls_token="<s>",sep_token="<s>",mask_token="<unk>",pad_token="</s>") |
|
e=mdl.resize_token_embeddings(len(tkz)) |
|
f=mdl.get_output_embeddings() |
|
with torch.no_grad(): |
|
for k,v in c.items(): |
|
e.weight[d["model"]["vocab"][k],:]=e.weight[v,:].sum(0) |
|
f.weight[d["model"]["vocab"][k],:]=f.weight[v,:].sum(0) |
|
mdl.set_input_embeddings(e) |
|
mdl.set_output_embeddings(f) |
|
mdl.save_pretrained(tgt) |
|
tkz.save_pretrained(tgt) |
|
EOF |
|
chmod 755 $TMPA |
|
$TMPA |
|
fi |
|
|
|
TMPB=./maker$$b.py |
|
cat << 'EOF' > $TMPB |
|
|
|
src="exSwallow-MS-7b-v0.1" |
|
tgt="KoichiYasuoka/Swallow-MS-7b-upos" |
|
from transformers import LlamaTokenizerFast,MistralForTokenClassification,AutoConfig,DataCollatorForTokenClassification,TrainingArguments,Trainer |
|
from tokenizers.normalizers import Replace |
|
|
|
class UPOSFileDataset(object): |
|
def __init__(self,conllu,tokenizer): |
|
self.conllu=open(conllu,"r",encoding="utf-8") |
|
self.tokenizer=tokenizer |
|
self.seeks=[0] |
|
self.multiword={} |
|
label=set(["SYM"]) |
|
s=self.conllu.readline() |
|
while s!="": |
|
if s=="\n": |
|
self.seeks.append(self.conllu.tell()) |
|
else: |
|
w=s.split("\t") |
|
if len(w)==10: |
|
if w[0].isdecimal(): |
|
label.add(w[3] if w[5]=="_" else w[3]+"|"+w[5]) |
|
elif w[0].find("-")>0: |
|
t=w[0].split("-") |
|
f,j,k=w[1],[],[] |
|
for i in range(int(t[0]),int(t[1])+1): |
|
w=self.conllu.readline().split("\t") |
|
j.append(w[3] if w[5]=="_" else w[3]+"|"+w[5]) |
|
k.append(w[1]) |
|
p="+".join(j) |
|
label.add(p) |
|
if p in self.multiword: |
|
self.multiword[p][f]=list(k) |
|
else: |
|
self.multiword[p]={f:list(k)} |
|
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 |
|
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): |
|
self.conllu.seek(self.seeks[i]) |
|
form,upos=[],[] |
|
while self.conllu.tell()<self.seeks[i+1]: |
|
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]) |
|
elif w[0].find("-")>0: |
|
t=w[0].split("-") |
|
u=[] |
|
for j in range(int(t[0]),int(t[1])+1): |
|
k=self.conllu.readline().split("\t") |
|
u.append(k[3] if k[5]=="_" else k[3]+"|"+k[5]) |
|
upos.append("+".join(u)) |
|
v=self.tokenizer(form,add_special_tokens=False) |
|
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) |
|
if len(i)<self.tokenizer.model_max_length-3: |
|
ids=[self.tokenizer.cls_token_id]+i+[self.tokenizer.sep_token_id] |
|
upos=["SYM"]+u+["SYM"] |
|
else: |
|
ids=i[0:self.tokenizer.model_max_length-2] |
|
upos=u[0:self.tokenizer.model_max_length-2] |
|
return {"input_ids":ids,"labels":[self.label2id[t] for t in upos]} |
|
|
|
tkz=LlamaTokenizerFast.from_pretrained(src) |
|
tkz.backend_tokenizer.normalizer=Replace(" ","\u2581") |
|
tkz.backend_tokenizer.model.byte_fallback=False |
|
trainDS=UPOSFileDataset("ja_gsd_modern.conllu",tkz) |
|
lid=trainDS.label2id |
|
cfg=AutoConfig.from_pretrained(src,num_labels=len(lid),label2id=lid,id2label={i:l for l,i in lid.items()},ignore_mismatched_sizes=True) |
|
dsp={"fp16":{"enabled":"auto"},"optimizer":{"type":"AdamW"},"scheduler":{"type":"WarmupLR","params":{}},"train_batch_size":"auto","train_micro_batch_size_per_gpu":"auto","zero_optimization":{"stage":3,"offload_optimizer":{"device":"cpu","pin_memory":True},"offload_param":{"device":"cpu","pin_memory":True},"overlap_comm":True,"contiguous_gradients":True,"reduce_bucket_size":"auto","stage3_prefetch_bucket_size":"auto","stage3_param_persistence_threshold":"auto","stage3_gather_16bit_weights_on_model_save":True}} |
|
arg=TrainingArguments(num_train_epochs=3,per_device_train_batch_size=8,deepspeed=dsp,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=DataCollatorForTokenClassification(tkz),model=MistralForTokenClassification.from_pretrained(src,config=cfg,ignore_mismatched_sizes=True),train_dataset=trainDS) |
|
trn.train() |
|
trn.save_model(tgt) |
|
tkz.save_pretrained(tgt) |
|
EOF |
|
chmod 755 $TMPB |
|
$TMPB |
|
exit |
|
|
