initial release

config.json

@@ -0,0 +1,162 @@
+{
+  "architectures": [
+    "MistralForTokenClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoModelForTokenClassification": "upos.MistralForTokenClassification"
+  },
+  "bos_token_id": 1,
+  "custom_pipelines": {
+    "upos": {
+      "impl": "upos.BellmanFordTokenClassificationPipeline",
+      "pt": "AutoModelForTokenClassification"
+    }
+  },
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "id2label": {
+    "0": "ADJ",
+    "1": "B-ADJ",
+    "2": "I-ADJ",
+    "3": "ADJ|Polarity=Neg",
+    "4": "B-ADJ|Polarity=Neg",
+    "5": "I-ADJ|Polarity=Neg",
+    "6": "ADP",
+    "7": "B-ADP",
+    "8": "I-ADP",
+    "9": "ADV",
+    "10": "B-ADV",
+    "11": "I-ADV",
+    "12": "AUX",
+    "13": "B-AUX",
+    "14": "I-AUX",
+    "15": "AUX|Polarity=Neg",
+    "16": "B-AUX|Polarity=Neg",
+    "17": "I-AUX|Polarity=Neg",
+    "18": "CCONJ",
+    "19": "B-CCONJ",
+    "20": "I-CCONJ",
+    "21": "DET",
+    "22": "B-DET",
+    "23": "I-DET",
+    "24": "INTJ",
+    "25": "B-INTJ",
+    "26": "I-INTJ",
+    "27": "NOUN",
+    "28": "B-NOUN",
+    "29": "I-NOUN",
+    "30": "NOUN|Polarity=Neg",
+    "31": "B-NOUN|Polarity=Neg",
+    "32": "I-NOUN|Polarity=Neg",
+    "33": "NUM",
+    "34": "B-NUM",
+    "35": "I-NUM",
+    "36": "PART",
+    "37": "B-PART",
+    "38": "I-PART",
+    "39": "PRON",
+    "40": "B-PRON",
+    "41": "I-PRON",
+    "42": "PROPN",
+    "43": "B-PROPN",
+    "44": "I-PROPN",
+    "45": "PUNCT",
+    "46": "B-PUNCT",
+    "47": "I-PUNCT",
+    "48": "SCONJ",
+    "49": "B-SCONJ",
+    "50": "I-SCONJ",
+    "51": "SYM",
+    "52": "B-SYM",
+    "53": "I-SYM",
+    "54": "VERB",
+    "55": "B-VERB",
+    "56": "I-VERB",
+    "57": "X",
+    "58": "B-X",
+    "59": "I-X"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "label2id": {
+    "ADJ": 0,
+    "ADJ|Polarity=Neg": 3,
+    "ADP": 6,
+    "ADV": 9,
+    "AUX": 12,
+    "AUX|Polarity=Neg": 15,
+    "B-ADJ": 1,
+    "B-ADJ|Polarity=Neg": 4,
+    "B-ADP": 7,
+    "B-ADV": 10,
+    "B-AUX": 13,
+    "B-AUX|Polarity=Neg": 16,
+    "B-CCONJ": 19,
+    "B-DET": 22,
+    "B-INTJ": 25,
+    "B-NOUN": 28,
+    "B-NOUN|Polarity=Neg": 31,
+    "B-NUM": 34,
+    "B-PART": 37,
+    "B-PRON": 40,
+    "B-PROPN": 43,
+    "B-PUNCT": 46,
+    "B-SCONJ": 49,
+    "B-SYM": 52,
+    "B-VERB": 55,
+    "B-X": 58,
+    "CCONJ": 18,
+    "DET": 21,
+    "I-ADJ": 2,
+    "I-ADJ|Polarity=Neg": 5,
+    "I-ADP": 8,
+    "I-ADV": 11,
+    "I-AUX": 14,
+    "I-AUX|Polarity=Neg": 17,
+    "I-CCONJ": 20,
+    "I-DET": 23,
+    "I-INTJ": 26,
+    "I-NOUN": 29,
+    "I-NOUN|Polarity=Neg": 32,
+    "I-NUM": 35,
+    "I-PART": 38,
+    "I-PRON": 41,
+    "I-PROPN": 44,
+    "I-PUNCT": 47,
+    "I-SCONJ": 50,
+    "I-SYM": 53,
+    "I-VERB": 56,
+    "I-X": 59,
+    "INTJ": 24,
+    "NOUN": 27,
+    "NOUN|Polarity=Neg": 30,
+    "NUM": 33,
+    "PART": 36,
+    "PRON": 39,
+    "PROPN": 42,
+    "PUNCT": 45,
+    "SCONJ": 48,
+    "SYM": 51,
+    "VERB": 54,
+    "X": 57
+  },
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "LlamaTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.1",
+  "use_cache": true,
+  "vocab_size": 43317
+}

maker.sh

@@ -0,0 +1,177 @@
+#! /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$$a.py
+     cat << 'EOF' > $TMPA
+#! /usr/bin/python3
+src="tokyotech-llm/Swallow-MS-7b-v0.1"
+tgt="exSwallow-MS-7b-v0.1"
+import json,torch,unicodedata
+from transformers import LlamaTokenizerFast,LlamaForCausalLM
+with open("JapaneseCoreKanji.txt","r",encoding="utf-8") as r:
+  cjk=[chr(int(t,16)) for t in r.read().strip().split("\n") if not t.startswith("#")]
+with open("ja_gsd_modern.conllu","r",encoding="utf-8") as r:
+  for s in r:
+    t=s.split("\t")
+    if len(t)==10:
+      for c in t[1]:
+        if unicodedata.name(c).startswith("CJK "):
+          cjk.append(c)
+cjk=list(set(cjk))
+tkz=LlamaTokenizerFast.from_pretrained(src,cls_token="<s>",sep_token="<s>",mask_token="<unk>",pad_token="</s>")
+c={i:j[2:] for i,j in zip(cjk,tkz(cjk)["input_ids"]) if len(j)>3}
+d=json.loads(tkz.backend_tokenizer.to_str())
+for i,j in enumerate(c,len(tkz)):
+  d["model"]["vocab"][j]=i
+tkz.backend_tokenizer.from_str(json.dumps(d)).save("tokenizer.json")
+mdl=LlamaForCausalLM.from_pretrained(src)
+tkz=LlamaTokenizerFast(tokenizer_file="tokenizer.json",model_max_length=mdl.config.max_position_embeddings,cls_token="<s>",sep_token="<s>",mask_token="<unk>",pad_token="</s>")
+e=mdl.resize_token_embeddings(len(tkz))
+f=mdl.get_output_embeddings()
+with torch.no_grad():
+  for k,v in c.items():
+    e.weight[d["model"]["vocab"][k],:]=e.weight[v,:].sum(0)
+    f.weight[d["model"]["vocab"][k],:]=f.weight[v,:].sum(0)
+mdl.set_input_embeddings(e)
+mdl.set_output_embeddings(f)
+mdl.save_pretrained(tgt)
+tkz.save_pretrained(tgt)
+EOF
+     chmod 755 $TMPA
+     $TMPA
+fi
+
+TMPB=./maker$$b.py
+cat << 'EOF' > $TMPB
+#! /usr/bin/env deepspeed
+src="exSwallow-MS-7b-v0.1"
+tgt="KoichiYasuoka/Swallow-MS-7b-upos"
+from transformers import LlamaTokenizerFast,MistralModel,MistralPreTrainedModel,AutoConfig,DataCollatorForTokenClassification,TrainingArguments,Trainer
+from transformers.modeling_outputs import TokenClassifierOutput
+from tokenizers.normalizers import Replace
+
+class MistralForTokenClassification(MistralPreTrainedModel):
+  def __init__(self,config):
+    from torch import nn
+    super().__init__(config)
+    self.num_labels=config.num_labels
+    self.model=MistralModel(config)
+    if hasattr(config,"classifier_dropout") and config.classifier_dropout is not None:
+      classifier_dropout=config.classifier_dropout
+    elif hasattr(config,"hidden_dropout") and config.hidden_dropout is not None:
+      classifier_dropout=config.hidden_dropout
+    else:
+      classifier_dropout=0.1
+    self.dropout=nn.Dropout(classifier_dropout)
+    self.classifier=nn.Linear(config.hidden_size,config.num_labels)
+    self.post_init()
+  def get_input_embeddings(self):
+    return self.model.embed_tokens
+  def set_input_embeddings(self,value):
+    self.model.embed_tokens=value
+  def forward(self,input_ids=None,past_key_values=None,attention_mask=None,position_ids=None,inputs_embeds=None,labels=None,use_cache=None,output_attentions=None,output_hidden_states=None,return_dict=None):
+    return_dict=return_dict if return_dict is not None else self.config.use_return_dict
+    transformer_outputs=self.model(input_ids,past_key_values=past_key_values,attention_mask=attention_mask,position_ids=position_ids,inputs_embeds=inputs_embeds,use_cache=use_cache,output_attentions=output_attentions,output_hidden_states=output_hidden_states,return_dict=return_dict)
+    hidden_states=transformer_outputs[0]
+    hidden_states=self.dropout(hidden_states)
+    logits=self.classifier(hidden_states)
+    loss=None
+    if labels is not None:
+      from torch import nn
+      loss_fct=nn.CrossEntropyLoss()
+      loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
+    if not return_dict:
+      output=(logits,)+transformer_outputs[1:]
+      return ((loss,)+output) if loss is not None else output
+    return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)
+
+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

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+    "model.norm.weight": "pytorch_model-00006-of-00006.bin"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,41 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<unk>",
+  "model_max_length": 4096,
+  "pad_token": "</s>",
+  "sep_token": "<s>",
+  "tokenizer_class": "LlamaTokenizerFast",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

upos.py

@@ -0,0 +1,76 @@
+from transformers import TokenClassificationPipeline,MistralModel,MistralPreTrainedModel
+from transformers.modeling_outputs import TokenClassifierOutput
+
+class BellmanFordTokenClassificationPipeline(TokenClassificationPipeline):
+  def __init__(self,**kwargs):
+    import numpy
+    super().__init__(**kwargs)
+    x=self.model.config.label2id
+    y=[k for k in x if not k.startswith("I-")]
+    self.transition=numpy.full((len(x),len(x)),numpy.nan)
+    for k,v in x.items():
+      for j in ["I-"+k[2:]] if k.startswith("B-") else [k]+y if k.startswith("I-") else y:
+        self.transition[v,x[j]]=0
+  def check_model_type(self,supported_models):
+    pass
+  def postprocess(self,model_outputs,**kwargs):
+    import numpy
+    if "logits" not in model_outputs:
+      return self.postprocess(model_outputs[0],**kwargs)
+    m=model_outputs["logits"][0].numpy()
+    e=numpy.exp(m-numpy.max(m,axis=-1,keepdims=True))
+    z=e/e.sum(axis=-1,keepdims=True)
+    for i in range(m.shape[0]-1,0,-1):
+      m[i-1]+=numpy.nanmax(m[i]+self.transition,axis=1)
+    k=[numpy.nanargmax(m[0])]
+    for i in range(1,m.shape[0]):
+      k.append(numpy.nanargmax(m[i]+self.transition[k[-1]]))
+    w=[{"entity":self.model.config.id2label[j],"start":s,"end":e,"score":z[i,j]} for i,((s,e),j) in enumerate(zip(model_outputs["offset_mapping"][0].tolist(),k)) if s<e]
+    if "aggregation_strategy" in kwargs and kwargs["aggregation_strategy"]!="none":
+      for i,t in reversed(list(enumerate(w))):
+        p=t.pop("entity")
+        if p.startswith("I-"):
+          w[i-1]["score"]=min(w[i-1]["score"],t["score"])
+          w[i-1]["end"]=w.pop(i)["end"]
+        elif p.startswith("B-"):
+          t["entity_group"]=p[2:]
+        else:
+          t["entity_group"]=p
+    for t in w:
+      t["text"]=model_outputs["sentence"][t["start"]:t["end"]]
+    return w
+
+class MistralForTokenClassification(MistralPreTrainedModel):
+  def __init__(self,config):
+    from torch import nn
+    super().__init__(config)
+    self.num_labels=config.num_labels
+    self.model=MistralModel(config)
+    if hasattr(config,"classifier_dropout") and config.classifier_dropout is not None:
+      classifier_dropout=config.classifier_dropout
+    elif hasattr(config,"hidden_dropout") and config.hidden_dropout is not None:
+      classifier_dropout=config.hidden_dropout
+    else:
+      classifier_dropout=0.1
+    self.dropout=nn.Dropout(classifier_dropout)
+    self.classifier=nn.Linear(config.hidden_size,config.num_labels)
+    self.post_init()
+  def get_input_embeddings(self):
+    return self.model.embed_tokens
+  def set_input_embeddings(self,value):
+    self.model.embed_tokens=value
+  def forward(self,input_ids=None,past_key_values=None,attention_mask=None,position_ids=None,inputs_embeds=None,labels=None,use_cache=None,output_attentions=None,output_hidden_states=None,return_dict=None):
+    return_dict=return_dict if return_dict is not None else self.config.use_return_dict
+    transformer_outputs=self.model(input_ids,past_key_values=past_key_values,attention_mask=attention_mask,position_ids=position_ids,inputs_embeds=inputs_embeds,use_cache=use_cache,output_attentions=output_attentions,output_hidden_states=output_hidden_states,return_dict=return_dict)
+    hidden_states=transformer_outputs[0]
+    hidden_states=self.dropout(hidden_states)
+    logits=self.classifier(hidden_states)
+    loss=None
+    if labels is not None:
+      from torch import nn
+      loss_fct=nn.CrossEntropyLoss()
+      loss=loss_fct(logits.view(-1,self.num_labels),labels.view(-1))
+    if not return_dict:
+      output=(logits,)+transformer_outputs[1:]
+      return ((loss,)+output) if loss is not None else output
+    return TokenClassifierOutput(loss=loss,logits=logits,hidden_states=transformer_outputs.hidden_states,attentions=transformer_outputs.attentions)