Compatiblity with fastai 1.0.47

fastai_contrib/text_data.py

@@ -6,7 +6,7 @@ import fastai
 from fastai.basic_data import DataBunch
 from fastai.core import ListRules, PathOrStr, IntsOrStrs, is_listy
 from fastai.data_block import ItemLists
-from fastai.text import Tokenizer, Vocab, SPProcessor, TextList, TextLMDataBunch
+from fastai.text import *
 
 
 class MosesPreprocessingFunc():
@@ -16,6 +16,109 @@ class MosesPreprocessingFunc():
     def __call__(self, t: str) -> str:
         return self.mt.tokenize(t, return_str=True, escape=True)
 
+try:
+    from fastai.text import SPProcessor
+except ImportError:
+
+    def _join_texts(texts:Collection[str], mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False):
+        if not isinstance(texts, np.ndarray): texts = np.array(texts)
+        if is1d(texts): texts = texts[:,None]
+        df = pd.DataFrame({i:texts[:,i] for i in range(texts.shape[1])})
+        bos_tok = f'{BOS} ' if include_bos else ''
+        text_col = f'{bos_tok}{FLD} {1} ' + df[0].astype(str) if mark_fields else f'{bos_tok}' + df[0].astype(str)
+        for i in range(1,len(df.columns)):
+            text_col += (f' {FLD} {i+1} ' if mark_fields else ' ') + df[i].astype(str)
+        if include_eos: text_col = text_col + f' {EOS}'
+        return text_col.values
+
+    def apply_rules(text, pre_rules=None, post_rules=None):
+        "Apply `pre_rules` and `post_rules` to `text`"
+        text = text.strip(' ')
+        for r in ifnone(pre_rules, defaults.text_pre_rules): text = r(text)
+        toks = text.split()
+        for r in ifnone(post_rules, defaults.text_post_rules): toks = r(toks)
+        return ' '.join(toks)
+
+    def get_default_size(texts, max_vocab_sz):
+        "Either max_vocab_sz or one quarter of the number of unique words in `texts`"
+        cnt = Counter()
+        for t in texts:
+            cnt.update(t.split())
+            if len(cnt)//4 > max_vocab_sz: return max_vocab_sz
+        res = len(cnt)//4
+        while res%8 != 0: res+=1
+        return res
+
+    full_char_coverage_langs = ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu",
+                           "it","lt","lv","mt","nl","pl","pt","ro","sk","sl","sv"] # all European langs
+
+    def train_sentencepiece(texts:Collection[str], path:PathOrStr, pre_rules: ListRules=None, post_rules:ListRules=None,
+        vocab_sz:int=None, max_vocab_sz:int=30000, model_type:str='unigram', max_sentence_len:int=20480, lang='en',
+        char_coverage=None, tmp_dir='tmp', enc='utf8'):
+        "Train a sentencepiece tokenizer on `texts` and save it in `path/tmp_dir`"
+        from sentencepiece import SentencePieceTrainer
+        cache_dir = Path(path)/tmp_dir
+        os.makedirs(cache_dir, exist_ok=True)
+        if vocab_sz is None: vocab_sz=get_default_size(texts, max_vocab_sz)
+        raw_text_path = cache_dir / 'all_text.out'
+        with open(raw_text_path, 'w', encoding=enc) as f: f.write("\n".join(texts))
+        spec_tokens = ['\u2581'+s for s in defaults.text_spec_tok]
+        SentencePieceTrainer.Train(" ".join([
+            f"--input={raw_text_path} --max_sentence_length={max_sentence_len}",
+            f"--character_coverage={ifnone(char_coverage, 0.99999 if lang in full_char_coverage_langs else 0.9998)}",
+            f"--unk_id={len(defaults.text_spec_tok)} --pad_id=-1 --bos_id=-1 --eos_id=-1",
+            f"--user_defined_symbols={','.join(spec_tokens)}",
+            f"--model_prefix={cache_dir/'spm'} --vocab_size={vocab_sz} --model_type={model_type}"]))
+        raw_text_path.unlink()
+        return cache_dir
+
+    class SPProcessor(PreProcessor):
+        "`PreProcessor` that tokenizes and numericalizes with `sentencepiece`"
+        def __init__(self, ds:ItemList=None, pre_rules: ListRules=None, post_rules:ListRules=None, vocab_sz:int=None,
+                     max_vocab_sz:int=30000, model_type:str='unigram', max_sentence_len:int=20480, lang='en',
+                     char_coverage=None, tmp_dir='tmp', mark_fields:bool=False, include_bos:bool=True,
+                     include_eos:bool=False, sp_model=None, sp_vocab=None, n_cpus:int=None, enc='utf8'):
+            try: from sentencepiece import SentencePieceTrainer,SentencePieceProcessor
+            except ImportError:
+                raise Exception('sentencepiece module is missing: run `pip install sentencepiece`')
+            self.pre_rules,self.post_rules,self.enc = pre_rules,post_rules,enc
+            self.mark_fields,self.include_bos,self.include_eos = mark_fields,include_bos,include_eos
+            self.sp_model,self.sp_vocab,self.n_cpus = sp_model,sp_vocab,ifnone(n_cpus,defaults.cpus)
+            self.train_func = partial(train_sentencepiece, pre_rules=pre_rules, post_rules=post_rules, vocab_sz=vocab_sz,
+                    max_vocab_sz=max_vocab_sz, model_type=model_type, max_sentence_len=max_sentence_len, lang=lang,
+                    char_coverage=char_coverage, tmp_dir=tmp_dir, enc=enc)
+
+        def process_one(self, item, join=True):
+            if join: text = _join_texts([item], self.mark_fields, self.include_bos, self.include_eos)[0]
+            text = apply_rules(text, pre_rules=self.pre_rules, post_rules=self.post_rules)
+            return self._encode_batch([text])[0]
+
+        def process(self, ds):
+            ds.items = _join_texts(ds.items, self.mark_fields, self.include_bos, self.include_eos)
+            ds.items = [apply_rules(t, pre_rules=self.pre_rules, post_rules=self.post_rules)
+                        for t in progress_bar(ds.items, leave=False)]
+            if self.sp_model is None or self.sp_vocab is None:
+                cache_dir = self.train_func(ds.items, ds.path)
+                self.sp_model,self.sp_vocab = cache_dir/'spm.model',cache_dir/'spm.vocab'
+            if not getattr(self, 'vocab', False):
+                with open(self.sp_vocab, 'r', encoding=self.enc) as f: self.vocab = Vocab([line.split('\t')[0] for line in f.readlines()])
+            if self.n_cpus <= 1: ds.items = self._encode_batch(ds.items)
+            else:
+                with ProcessPoolExecutor(self.n_cpus) as e:
+                    ds.items = np.array(sum(e.map(self._encode_batch, partition_by_cores(ds.items, self.n_cpus)), []))
+            ds.vocab = self.vocab
+
+        def _encode_batch(self, texts):
+            from sentencepiece import SentencePieceProcessor
+            tok = SentencePieceProcessor()
+            tok.Load(str(self.sp_model))
+            return [np.array(tok.EncodeAsIds(t)) for t in texts]
+
+        @classmethod
+        def load(cls, path:PathOrStr, tmp_dir:PathOrStr='tmp', name:str='spm'):
+            cache_dir = Path(path)/tmp_dir
+            return cls(sp_model=cache_dir/f'{name}.model', sp_vocab=cache_dir/f'{name}.vocab')
+
 class SPProcessor2(SPProcessor):
     def process(self, ds):
         super().process(ds)