def main(): # Instantiate argument parser parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--train_data_file", default=None, type=str, required=True, help= "The input training data file or a path to a directory with multiple training data files." ) parser.add_argument( "--output_dir", type=str, required=True, help="The output directory where the tokenizer model will be written.") # Optional parameters parser.add_argument("--vocab_size", default=5000, type=int, help="Vocabulary maximum size, default 5000.") parser.add_argument("--min_freq", default=2, type=int, help="Minimum number of occurrences, default 2") # Generate args args = parser.parse_args() # Initialize a tokenizer tokenizer = ByteLevelBPETokenizer() # Get training files paths = os.path.abspath(args.train_data_file) if not args.train_data_file.endswith(".txt"): paths = [str(x) for x in Path(paths).glob("**/*.txt")] # Customize training tokenizer.train(files=paths, vocab_size=args.vocab_size, min_frequency=args.min_freq, special_tokens=[ "<s>", "<pad>", "</s>", "<unk>", "<mask>", ]) tokenizer.add_special_tokens(["<x>", "<z>"]) # Save files to disk output_dir = os.path.abspath(args.output_dir) if not os.path.exists(output_dir): os.makedirs(output_dir) tokenizer.save_model(output_dir)
def load_tokenizer(vocab='./tokenizer/vocab.json', merges='./tokenizer/merges.txt', gpt=False, load_from=None): if gpt: if load_from: tokenizer = GPT2Tokenizer.from_pretrained(load_from) else: tokenizer = GPT2Tokenizer( vocab, merges, bos_token=CARD_BEGIN, eos_token=CARD_END, sep_token=CARD_END, unk_token=UNK, pad_token=CARD_PAD, mask_token=CARD_MASK, padding_side="left" ) else: tokenizer = ByteLevelBPETokenizer(vocab, merges) tokenizer.add_special_tokens(SPECIAL_TOKENS + OTHER_TOKENS) tokenizer.mask_token = CARD_MASK tokenizer.pre_tokenizer = Whitespace() return tokenizer
class ByteBPETokenizer: def __init__(self, vocab_json, merge_txt, max_length=750): self.tokenizer = ByteLevelBPETokenizer(vocab_json, merge_txt) self.tokenizer.enable_truncation(max_length=max_length) self.tokenizer.enable_padding(max_length=max_length) self.tokenizer.add_special_tokens(["[PAD]", "[CLS]"]) # self.tokenizer.post_processor = RobertaProcessing(("</s>", 2), ("<s>", 1)) # self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base') def encode(self, review): review = clean_sentence(review) encoded = self.tokenizer.encode(review.lower()) # pp_encoded = self.tokenizer.post_process(encoded) return encoded def tokenize2Index(self, review, should_stem=False): encoded = self.encode(review) return encoded.ids def trainBPE(self, paths, vocab_size=30000, min_frequency=10, special_tokens=["[PAD]", "[CLS]"]): tokenizer = ByteLevelBPETokenizer() tokenizer.train(files=paths, vocab_size=vocab_size, min_frequency=min_frequency, special_tokens=special_tokens) tokenizer.save("yelp_bpe/", "yelp-bpe")
def create_tokenizer(corpus_file_path, vocab_size): tokenizer = ByteLevelBPETokenizer() tokenizer.train(corpus_file_path, vocab_size) tokenizer.add_special_tokens(['<SOS>', '<PAD>', '<EOS>']) return tokenizer
class HuggingFaceBpeHelper(BPEHelper): """ HuggingFace's ByteLevelBPE Tokenizer. Fast because Rust. """ def __init__(self, opt: Opt, shared: TShared = None): super().__init__(opt, shared) # Default true for HF self.special_tok_map = {} # map from HF self.add_prefix_space = opt.get('bpe_add_prefix_space', True) if self.add_prefix_space is None: self.add_prefix_space = True if opt.get('dict_loaded'): dfname = opt['dict_file'] if PathManager.exists(f'{dfname}-merges.txt'): opt['bpe_merge'] = f'{dfname}-merges.txt' if PathManager.exists(f'{dfname}-vocab.json'): opt['bpe_vocab'] = f'{dfname}-vocab.json' try: from tokenizers import ByteLevelBPETokenizer except ImportError: raise ImportError( 'Please install HuggingFace tokenizer with: pip install tokenizers' ) if self.bpe_dropout: raise NotImplementedError( '--bpe-dropout is not supported with ByteLevelBPE because tokenizers ' 'library does not allow dynamically turning BPE on/off. You can use ' '--dict-tokenizer slow_bytelevel_bpe to gain this feature.' ) if self.lower: warn_once('Are you sure you want to lower case your BPE dictionary?') if self.maxtokens > 0 or self.minfreq > 0: raise ValueError( 'You should not filter vocabulary with using --dict-tokenizer bytelevelbpe' ' (no --dict-minfreq or --dict-maxtokens).' ) if 'bpe_vocab' not in opt: raise ValueError('--bpe-vocab is required for loading pretrained tokenizer') if 'bpe_merge' not in opt: raise ValueError('--bpe-merge is required for loading pretrained tokenizer') self.vocab_path = opt['bpe_vocab'] self.merge_path = opt['bpe_merge'] if not self.vocab_path or not self.merge_path: raise IOError( '--bpe-vocab and --bpe-merge are mandatory with ' '--dict-tokenizer bytelevelbpe' ) if not PathManager.exists(self.vocab_path): raise IOError( f'File {self.vocab_path} does not exist. --bpe-vocab must be pretrained.' ) if not PathManager.exists(self.merge_path): raise IOError( f'File {self.merge_path} does not exist. --bpe-merge must be pretrained.' ) self.tokenizer = ByteLevelBPETokenizer( self.vocab_path, self.merge_path, self.add_prefix_space ) def helper_encode(self, text: str) -> List[str]: """ Decode list of tokens into text string. :param tokens: list of tokens :param delimiter: string delimiter for tokens :return text: decoded text """ return self.tokenizer.encode(text).tokens def helper_decode( self, tokens: List[str], token_ids: List[int], delimiter: str ) -> str: """ Decode list of tokens into text string. :param tokens: list of tokens :param token_ids: list of token ids :param delimiter: string delimiter for tokens :return text: decoded text """ text = self.tokenizer.decode(token_ids, skip_special_tokens=False) return text def add_special_tokens(self, dict_agent, special_tokens: List[str]): """ Add special tokens to the tokenizer and dict_agent. """ logging.debug(f'adding the following special tokens: {special_tokens}') self.tokenizer.add_special_tokens(special_tokens) # add to HF for tok in special_tokens: parlai_key = dict_agent[tok] hf_key = self.tokenizer.token_to_id(tok) self.special_tok_map[parlai_key] = hf_key def sync_with_dict(self, dict_agent): """ Sync the dictionary agent with Hugging Face tokenizer's BPE dict. Called only once on initialization. """ special_tokens = [ dict_agent.null_token, dict_agent.start_token, dict_agent.end_token, dict_agent.unk_token, ] self.add_special_tokens(dict_agent, special_tokens) for i in range(self.tokenizer.get_vocab_size() - len(special_tokens)): token = self.tokenizer.id_to_token(i) dict_agent.add_token(token) # We don't have access to the hugging face word frequency table, # just set it to 1 instead dict_agent.freq[token] = 1 def save(self, dir_name: str, file_name: str): """ Save appropriate files. :param dir_name: directory to save. :param file_name: file to save. """ self.tokenizer.save_model(dir_name, file_name)
if TRAIN_BASE: # Initialize a tokenizer tokenizer = ByteLevelBPETokenizer() # Customize training tokenizer.train(files=paths, vocab_size=52_000, min_frequency=2, special_tokens=[ "<s>", "<pad>", "</s>", "<unk>", "<mask>", ]) # Save files to disk tokenizer.save_model("tokenizer") inp = 'print("Hello World!")' tokenizer = GPT2Tokenizer.from_pretrained('tokenizer') tokenizer.add_special_tokens({ "bos_token": "<s>", "pad_token": "<pad>", "eos_token": "</s>", "unk_token": "<unk>", "mask_token": "<mask>", }) t = tokenizer.encode(inp) print(t)
class HuggingFaceBpeHelper(BPEHelper): """ HuggingFace's ByteLevelBPE Tokenizer. Fast because Rust. """ def __init__(self, opt: Opt, shared: TShared = None): super().__init__(opt, shared) # Default true for HF self.add_prefix_space = opt.get('bpe_add_prefix_space', True) if self.add_prefix_space is None: self.add_prefix_space = True if opt.get('dict_loaded'): dfname = opt['dict_file'] if os.path.isfile(f'{dfname}-merges.txt'): opt['bpe_merge'] = f'{dfname}-merges.txt' if os.path.isfile(f'{dfname}-vocab.json'): opt['bpe_vocab'] = f'{dfname}-vocab.json' try: from tokenizers import ByteLevelBPETokenizer except ImportError: raise ImportError( 'Please install HuggingFace tokenizer with: pip install tokenizers' ) if self.lower: raise ValueError( 'Only use --dict-lower false with --dict-tokenizer bytelevelbpe' ) if self.maxtokens > 0 or self.minfreq > 0: raise ValueError( 'You should not filter vocabulary with using --dict-tokenizer bytelevelbpe' ' (no --dict-minfreq or --dict-maxtokens).') if 'bpe_vocab' not in opt: raise ValueError( '--bpe-vocab is required for loading pretrained tokenizer') if 'bpe_merge' not in opt: raise ValueError( '--bpe-merge is required for loading pretrained tokenizer') self.vocab_path = opt['bpe_vocab'] self.merge_path = opt['bpe_merge'] if not self.vocab_path or not self.merge_path: raise IOError('--bpe-vocab and --bpe-merge are mandatory with ' '--dict-tokenizer bytelevelbpe') if not os.path.isfile(self.vocab_path): raise IOError( f'File {self.vocab_path} does not exist. --bpe-vocab must be pretrained.' ) if not os.path.isfile(self.merge_path): raise IOError( f'File {self.merge_path} does not exist. --bpe-merge must be pretrained.' ) self.tokenizer = ByteLevelBPETokenizer(self.vocab_path, self.merge_path, self.add_prefix_space) def helper_encode(self, text: str) -> List[str]: """ Decode list of tokens into text string. :param tokens: list of tokens :param delimiter: string delimiter for tokens :return text: decoded text """ return self.tokenizer.encode(text).tokens def helper_decode(self, tokens: List[str], token_ids: List[int], delimiter: str) -> str: """ Decode list of tokens into text string. :param tokens: list of tokens :param token_ids: list of token ids :param delimiter: string delimiter for tokens :return text: decoded text """ text = self.tokenizer.decode(token_ids) return text def sync_with_dict(self, dict_agent): """ Sync the dictionary agent with Hugging Face tokenizer's BPE dict. Called only once on initialization. """ special_tokens = [ dict_agent.null_token, dict_agent.start_token, dict_agent.end_token, dict_agent.unk_token, ] self.tokenizer.add_special_tokens(special_tokens) for i in range(self.tokenizer.get_vocab_size() - 4): token = self.tokenizer.id_to_token(i) dict_agent.add_token(token) # We don't have access to the hugging face word frequency table, # just set it to 1 instead dict_agent.freq[token] = 1 def save(self, dir_name: str, file_name: str): """ Save appropriate files. :param dir_name: directory to save. :param file_name: file to save. """ self.tokenizer.save(dir_name, file_name)
class CodeTrainedBPE_Translation_DataProcessor(DataProcessor, Dataset): def __init__(self, task_data, max_src_len=512, max_tgt_len=512): """ This data processor tokenizes and numericalises using a custom byte pair encoding trained on the codeSearchNet train data with full docstrings. """ self.task_data = task_data self.max_src_len = max_src_len self.max_tgt_len = max_tgt_len self.tokenizer = ByteLevelBPETokenizer( "/nfs/phd_by_carlos/notebooks/datasets/code_search_net/code_bpe_hugging_32k-vocab.json", "/nfs/phd_by_carlos/notebooks/datasets/code_search_net/code_bpe_hugging_32k-merges.txt" ) self.tokenizer.add_special_tokens(["[CLS]", "[SOS]", "[EOS]", "[PAD]"]) self.SOS = self.tokenizer.encode("[SOS]").ids[0] self.EOS = self.tokenizer.encode("[EOS]").ids[0] self.PAD = self.tokenizer.encode("[PAD]").ids[0] self.CLS = self.tokenizer.encode("[CLS]").ids[0] self.__remove_long_samples() def __len__(self): return len(self.task_data) def __getitem__(self, idx): src, tgt = self.task_data[idx] sample = {'src': self.encode(src), 'tgt': self.encode(tgt)} return sample @property def vocab_size(self): return self.tokenizer.get_vocab_size() def __remove_long_samples(self): for i in tqdm.tqdm(list(reversed(range(len(self.task_data)))), desc="removing long samples"): src, tgt = self.task_data[i] if len(self.encode(src)) > self.max_src_len or len( self.encode(tgt)) > self.max_tgt_len: del self.task_data[i] def encode(self, sample): """ sample: str: the input string to encode """ return [self.SOS] + self.tokenizer.encode(sample).ids + [self.EOS] def encode_src(self, sample): return self.encode(sample) def encode_tgt(self, sample): return self.encode(sample) def encode_to_tensor(self, input_samples): """ input_samples: [str]: one or more strings to convert to a single padded tensor. (Seq_len x batch) """ return pad_sequence([ torch.Tensor(self.encode(sample)).type(torch.LongTensor) for sample in input_samples ], padding_value=self.PAD) def collate(self, input_samples): """ input_samples: [dict]: these are samples obtained through the _get_item method """ collated_samples = {} sample_keys = input_samples[0].keys() for key in sample_keys: collated_samples[key] = torch.nn.utils.rnn.pad_sequence( [ torch.Tensor(sample[key]).type(torch.LongTensor) for sample in input_samples ], padding_value=self.PAD) return collated_samples def decode(self, ids): """ ids: [int]: ids to decode """ return self.tokenizer.decode(ids) def decode_src(self, ids): return self.decode(ids) def decode_tgt(self, ids): return self.decode(ids) def validate_prediction(self, numerical_sequence): # there are no constraints return True def prediction_is_complete(self, numerical_sequence): return self.EOS in numerical_sequence def decode_tensor(self, output_tensor): """ output_tensor: [[int]]: model output (Seq_len x batch) """ batch_first_output_tensor = output_tensor.T return [ self.decode(sequence.cpu().tolist()) for sequence in batch_first_output_tensor ] def to_dataloader(self, batch_size, repeat=False, num_workers=4, shuffle=True): """ This function returns an iterable object with all the data batched. >>> BPE_processor = CodeTrainedBPE_Translation_DataProcessor(validation_pairs, max_tgt_len=100) >>> dataloader = BPE_processor.to_dataloader(2) >>> for i_batch, sample_batched in enumerate(dataloader): >>> print(sample_batched["tgt"]) >>> print(BPE_processor.decode_tensor(sample_batched["tgt"])) >>> break """ return DataLoader(self, batch_size=batch_size, num_workers=num_workers,\ drop_last=False, collate_fn = self.collate, shuffle=shuffle) def save(self, path): torch.save(self, path)
class Parse_Tree_Translation_DataProcessor(Dataset): def __init__( self, task_data, max_length=500, tokenizer_dir="/nfs/phd_by_carlos/notebooks/datasets/code_search_net/", grammar_path="src/tree-sitter/tree-sitter-python/src/grammar.json", **kwargs): self.task_data = task_data self.max_length = max_length self.tokenizer = ByteLevelBPETokenizer( tokenizer_dir + "code_bpe_hugging_32k-vocab.json", tokenizer_dir + "code_bpe_hugging_32k-merges.txt") self.tokenizer.add_special_tokens(["[CLS]", "[SOS]", "[EOS]", "[PAD]"]) self.SOS = self.tokenizer.encode("[SOS]").ids[0] self.EOS = self.tokenizer.encode("[EOS]").ids[0] self.PAD = self.tokenizer.encode("[PAD]").ids[0] self.CLS = self.tokenizer.encode("[CLS]").ids[0] with open(grammar_path, "r") as grammar_file: self.python_grammar = json.load(grammar_file) extra_externals = { "_string_start": { "type": "PATTERN", "value": '"' }, "_string_content": { "type": "PATTERN", "value": "[A-Za-z0-9 _,.()\/{}!$@'*]*" }, "_string_end": { "type": "PATTERN", "value": '"' }, "_newline": { "type": "BLANK" } } for node_type, member in extra_externals.items(): self.python_grammar["rules"][node_type] = member self.python_parser = Code_Parser(self.python_grammar, "python", **kwargs) self.node_processor = Node_Processor() self.tree_vocab, grammar_patterns = get_grammar_vocab( self.python_grammar) self.tokenizer.add_tokens(["<REDUCE>"]) for tree_token in sorted(self.tree_vocab): if len(self.tokenizer.encode(tree_token).tokens) != 1: self.tokenizer.add_tokens([tree_token]) # filtering the data filtered_task_data = [] for desc, code in self.task_data: numerical_code_sequence = self.encode_tgt(code) numerical_desc_sequence = self.encode_src(desc) token_sequence = self.numerical_to_token_sequence( numerical_code_sequence) if self.python_parser.is_valid_sequence(token_sequence) and len( token_sequence) <= max_length and len( numerical_desc_sequence) <= max_length: filtered_task_data.append((desc, code)) elif len(token_sequence) > max_length or len( numerical_desc_sequence) > max_length: print( f"Sequence too long: src->{len(numerical_desc_sequence)}, tgt->{len(token_sequence)}" ) else: print(f"Could not parse and reconstruct: {code}") self.task_data = filtered_task_data def __len__(self): return len(self.task_data) def __getitem__(self, idx): if idx >= len(self): raise IndexError src, tgt = self.task_data[idx] sample = {'src': self.encode_src(src), 'tgt': self.encode_tgt(tgt)} return sample @property def vocab_size(self): return self.tokenizer.get_vocab_size() def encode_src(self, desc_str): return [self.SOS] + self.tokenizer.encode(desc_str).ids + [self.EOS] def encode_tgt(self, code_str): code_sequence = self.python_parser.code_to_sequence(code_str) numerical_code = [] for code_token in code_sequence: numerical_code += self.tokenizer.encode(code_token).ids return [self.SOS] + numerical_code + [self.EOS] def decode_src(self, numerical_desc): """ ids: [int]: ids to decode """ return self.tokenizer.decode(ids) def numerical_to_token_sequence(self, numerical_code): token_sequence = [ self.tokenizer.decode([token_idx]) for token_idx in numerical_code if token_idx not in [self.SOS, self.EOS, self.PAD, self.CLS] ] return token_sequence def decode_tgt(self, numerical_code): token_sequence = self.numerical_to_token_sequence(numerical_code) partial_tree = self.python_parser.sequence_to_partial_tree( token_sequence) return self.node_processor.pretty_print( partial_tree.root), partial_tree def validate_prediction(self, current_prediction): # print(f"validating: {current_prediction}") token_sequence = self.numerical_to_token_sequence(current_prediction) return self.python_parser.is_valid_sequence(token_sequence) def prediction_is_complete(self, current_prediction): token_sequence = self.numerical_to_token_sequence(current_prediction) return self.python_parser.sequence_to_partial_tree( token_sequence).is_complete def collate(self, input_samples): """ input_samples: [dict]: these are samples obtained through the _get_item method """ collated_samples = {} sample_keys = input_samples[0].keys() for key in sample_keys: collated_samples[key] = torch.nn.utils.rnn.pad_sequence( [ torch.Tensor(sample[key]).type(torch.LongTensor) for sample in input_samples ], padding_value=self.PAD) return collated_samples def to_dataloader(self, batch_size, num_workers=4, shuffle=True): """ This function returns an iterable object with all the data batched. >>> BPE_processor = CodeTrainedBPE_Translation_DataProcessor(validation_pairs, max_tgt_len=100) >>> dataloader = BPE_processor.to_dataloader(2) >>> for i_batch, sample_batched in enumerate(dataloader): >>> print(sample_batched["tgt"]) >>> print(BPE_processor.decode_tensor(sample_batched["tgt"])) >>> break """ return DataLoader(self, batch_size=batch_size, num_workers=num_workers,\ drop_last=False, collate_fn = self.collate, shuffle=shuffle) def save(self, path): torch.save(self, path)
class HuggingfaceTokenizerBPE(nn.Module): def __init__(self, text_files, dataset_info_path='', config_data=None): super().__init__() # The default vocab size in the BERT model is 30522. If we want a number larger than that, we will also have to # change the BERT configuration. vocab_size = 30000 self.info = f'hug{vocab_size}' with open(f'config/data/{config_data}.json') as json_file: tokenizer_from = json.load(json_file)['tokenizer_from'] config_name = config_data if tokenizer_from == "" else tokenizer_from print( os.path.join(dataset_info_path, f'tokenizer_{config_name}_{vocab_size}-vocab.json')) # The loading is only properly implemented starting from version 0.8. However, it makes the system use a lot of # CPU for no reason (it is much slower). Maybe it will be fixed in the future. if not os.path.isfile( os.path.join( dataset_info_path, f'tokenizer_{config_name}_{vocab_size}-vocab.json')): text_files = text_files() self.tokenizer = ByteLevelBPETokenizer() # Join into a single file. This should NOT be necessary but it does not work properly with a lot of files with open('/tmp/text_files.txt', 'wb') as outfile: for filename in tqdm( text_files, desc='Joining all files into one for tokenization'): with open(filename, 'rb') as readfile: shutil.copyfileobj(readfile, outfile) text_files = '/tmp/text_files.txt' self.tokenizer.train(text_files, vocab_size=vocab_size, special_tokens=special_tokens) self.tokenizer.save(dataset_info_path, f'tokenizer_{config_name}_{vocab_size}') # No "else", always load for consistency vocab_file = os.path.join( dataset_info_path, f'tokenizer_{config_name}_{vocab_size}-vocab.json') merges_file = os.path.join( dataset_info_path, f'tokenizer_{config_name}_{vocab_size}-merges.txt') self.tokenizer = ByteLevelBPETokenizer(vocab_file=vocab_file, merges_file=merges_file) self.tokenizer.add_special_tokens(special_tokens) self.index_special_tokens = { tok: self.tokenizer.encode(tok).ids[0] for tok in special_tokens } @property def device(self): return self._float_tensor.device def encode(self, sentence: str): output = self.tokenizer.encode(sentence) token_ids = output.ids tokens = output.tokens return torch.tensor(token_ids), tokens def decode(self, tokens: torch.LongTensor): assert tokens.dim() == 1 tokens = list(tokens.cpu().numpy()) sentences = self.tokenizer.decode(tokens) return sentences def id_to_token(self, token_id): if type(token_id) != torch.Tensor: token_id = torch.tensor(token_id) return self.tokenizer.id_to_token(token_id) def token_to_id(self, token): assert type(token) == str return self.tokenizer.token_to_id(token) def __len__(self): return self.tokenizer.get_vocab_size() # This is simply for PyCharm to find the correct reference to the methods of the class def __call__(self, *input, **kwargs) -> typing.Any: return super().__call__(*input, **kwargs)