Source code for espnet2.bin.lm_inference

#!/usr/bin/env python3
import argparse
import logging
import sys
from pathlib import Path
from typing import Any, List, Optional, Sequence, Tuple, Union

import numpy as np
import torch
import torch.quantization
from typeguard import typechecked

from espnet2.fileio.datadir_writer import DatadirWriter
from espnet2.tasks.lm import LMTask
from espnet2.text.build_tokenizer import build_tokenizer
from espnet2.text.token_id_converter import TokenIDConverter
from espnet2.text.whisper_token_id_converter import OpenAIWhisperTokenIDConverter
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.utils import config_argparse
from espnet2.utils.types import str2bool, str2triple_str, str_or_none
from espnet.nets.batch_beam_search import BatchBeamSearch
from espnet.nets.beam_search import BeamSearch, Hypothesis
from espnet.nets.scorer_interface import BatchScorerInterface
from espnet.nets.scorers.length_bonus import LengthBonus
from espnet.utils.cli_utils import get_commandline_args

# Alias for typing
ListOfHypothesis = List[

[docs]class GenerateText: """GenerateText class Examples: >>> generatetext = GenerateText( lm_train_config="lm_config.yaml", lm_file="lm.pth", token_type="bpe", bpemodel="bpe.model", ) >>> prompt = "I have travelled to many " >>> generatetext(prompt) [(text, token, token_int, hypothesis object), ...] """ @typechecked def __init__( self, lm_train_config: Union[Path, str, None] = None, lm_file: Union[Path, str, None] = None, ngram_scorer: str = "full", ngram_file: Union[Path, str, None] = None, token_type: Optional[str] = None, bpemodel: Optional[str] = None, device: str = "cpu", maxlen: int = 100, minlen: int = 0, batch_size: int = 1, dtype: str = "float32", beam_size: int = 20, ngram_weight: float = 0.0, penalty: float = 0.0, nbest: int = 1, quantize_lm: bool = False, quantize_modules: List[str] = ["Linear"], quantize_dtype: str = "qint8", ): # 1. Build language model lm, lm_train_args = LMTask.build_model_from_file( lm_train_config, lm_file, device ), dtype)).eval() if quantize_lm:"Use quantized LM for decoding.") lm = torch.quantization.quantize_dynamic( lm, qconfig_spec=set([getattr(torch.nn, q) for q in quantize_modules]), dtype=getattr(torch, quantize_dtype), ) token_list = lm_train_args.token_list # 2. Build ngram model if ngram_file is not None: if ngram_scorer == "full": from espnet.nets.scorers.ngram import NgramFullScorer ngram = NgramFullScorer(ngram_file, token_list) else: from espnet.nets.scorers.ngram import NgramPartScorer ngram = NgramPartScorer(ngram_file, token_list) else: ngram = None # 3. Build BeamSearch object scorers = dict( lm=lm.lm, ngram=ngram, length_bonus=LengthBonus(len(token_list)), ) weights = dict( lm=1.0, ngram=ngram_weight, length_bonus=penalty, ) beam_search = BeamSearch( scorers=scorers, weights=weights, beam_size=beam_size, vocab_size=len(token_list), sos=lm.sos_ids[0], # not really used eos=lm.eos_id, token_list=token_list, pre_beam_score_key="full", ) # TODO(karita): make all scorers batchfied if batch_size == 1: non_batch = [ k for k, v in beam_search.full_scorers.items() if not isinstance(v, BatchScorerInterface) ] if len(non_batch) == 0: beam_search.__class__ = BatchBeamSearch"BatchBeamSearch implementation is selected.") else: logging.warning( f"As non-batch scorers {non_batch} are found, " f"fall back to non-batch implementation." ), dtype=getattr(torch, dtype)).eval() for scorer in scorers.values(): if isinstance(scorer, torch.nn.Module):, dtype=getattr(torch, dtype)).eval()"Beam_search: {beam_search}")"Decoding device={device}, dtype={dtype}") # 4. [Optional] Build Text converter: e.g. bpe-sym -> Text if token_type is None: token_type = lm_train_args.token_type if bpemodel is None: bpemodel = lm_train_args.bpemodel if token_type is None: tokenizer = None elif ( token_type == "bpe" or token_type == "hugging_face" or "whisper" in token_type ): if bpemodel is not None: tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel) else: tokenizer = None else: tokenizer = build_tokenizer(token_type=token_type) if bpemodel not in ["whisper_en", "whisper_multilingual"]: converter = TokenIDConverter(token_list=token_list) else: converter = OpenAIWhisperTokenIDConverter(model_type=bpemodel) beam_search.set_hyp_primer( list(converter.tokenizer.sot_sequence_including_notimestamps) )"Text tokenizer: {tokenizer}") self.lm = lm self.lm_train_args = lm_train_args self.converter = converter self.tokenizer = tokenizer self.beam_search = beam_search self.maxlen = maxlen self.minlen = minlen self.device = device self.dtype = dtype self.nbest = nbest @torch.no_grad() @typechecked def __call__(self, text: Union[str, torch.Tensor, np.ndarray]) -> ListOfHypothesis: """Inference Args: text: Input text used as condition for generation If text is str, it will be converted to token ids and a <sos> token will be added at the beginning. If text is Tensor or ndarray, it will be used directly. Returns: List of (text, token, token_int, hyp) """ if isinstance(text, str): tokens = self.tokenizer.text2tokens(text) token_ids = self.converter.tokens2ids(tokens) else: token_ids = text.tolist() hyp_primer = token_ids[1:] # remove initial space in BPE self.beam_search.set_hyp_primer(hyp_primer)"hyp primer: {hyp_primer}") nbest_hyps = self.beam_search( x=torch.zeros(1, 1, device=self.device), # only used to obtain device info maxlenratio=-self.maxlen, # negative int means a constant max length minlenratio=-self.minlen, # same for min length ) nbest_hyps = nbest_hyps[: self.nbest] results = [] for hyp in nbest_hyps: assert isinstance(hyp, Hypothesis), type(hyp) # remove sos/eos and convert to list token_int = hyp.yseq[:-1] if not isinstance(token_int, list): token_int = token_int.tolist() # remove blank symbol id, which is assumed to be 0 token_int = list(filter(lambda x: x != 0, token_int)) # Change integer-ids to tokens token = self.converter.ids2tokens(token_int) _text = None if self.tokenizer is not None: _text = self.tokenizer.tokens2text(token) results.append((_text, token, token_int, hyp)) return results
[docs] @staticmethod def from_pretrained( model_tag: Optional[str] = None, **kwargs: Optional[Any], ): """Build GenerateText instance from the pretrained model. Args: model_tag (Optional[str]): Model tag of the pretrained models. Currently, the tags of espnet_model_zoo are supported. Returns: GenerateText: GenerateText instance. """ if model_tag is not None: try: from espnet_model_zoo.downloader import ModelDownloader except ImportError: logging.error( "`espnet_model_zoo` is not installed. " "Please install via `pip install -U espnet_model_zoo`." ) raise d = ModelDownloader() kwargs.update(**d.download_and_unpack(model_tag)) return GenerateText(**kwargs)
[docs]@typechecked def inference( output_dir: str, maxlen: int, minlen: int, batch_size: int, dtype: str, beam_size: int, ngpu: int, seed: int, ngram_weight: float, penalty: float, nbest: int, num_workers: int, log_level: Union[int, str], data_path_and_name_and_type: Sequence[Tuple[str, str, str]], key_file: Optional[str], lm_train_config: Optional[str], lm_file: Optional[str], word_lm_train_config: Optional[str], word_lm_file: Optional[str], ngram_file: Optional[str], model_tag: Optional[str], token_type: Optional[str], bpemodel: Optional[str], allow_variable_data_keys: bool, quantize_lm: bool, quantize_modules: List[str], quantize_dtype: str, ): if batch_size > 1: raise NotImplementedError("batch decoding is not implemented") if word_lm_train_config is not None: raise NotImplementedError("Word LM is not implemented") if ngpu > 1: raise NotImplementedError("only single GPU decoding is supported") logging.basicConfig( level=log_level, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) if ngpu >= 1: device = "cuda" else: device = "cpu" # 1. Set random seed set_all_random_seed(seed) # 2. Build generatetext generatetext_kwargs = dict( lm_train_config=lm_train_config, lm_file=lm_file, ngram_file=ngram_file, token_type=token_type, bpemodel=bpemodel, device=device, maxlen=maxlen, minlen=minlen, dtype=dtype, beam_size=beam_size, ngram_weight=ngram_weight, penalty=penalty, nbest=nbest, quantize_lm=quantize_lm, quantize_modules=quantize_modules, quantize_dtype=quantize_dtype, ) generatetext = GenerateText.from_pretrained( model_tag=model_tag, **generatetext_kwargs, ) # 3. Build data iterator loader = LMTask.build_streaming_iterator( data_path_and_name_and_type, dtype=dtype, batch_size=batch_size, key_file=key_file, num_workers=num_workers, preprocess_fn=LMTask.build_preprocess_fn(generatetext.lm_train_args, False), collate_fn=LMTask.build_collate_fn(generatetext.lm_train_args, False), allow_variable_data_keys=allow_variable_data_keys, inference=True, ) # 4. Start for-loop # FIXME(kamo): The output format should be discussed about with DatadirWriter(output_dir) as writer: for keys, batch in loader: assert isinstance(batch, dict), type(batch) assert all(isinstance(s, str) for s in keys), keys _bs = len(next(iter(batch.values()))) assert len(keys) == _bs, f"{len(keys)} != {_bs}" batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")} # N-best list of (text, token, token_int, hyp_object) results = generatetext(**batch) # Only supporting batch_size==1 key = keys[0] for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results): # Create a directory: outdir/{n}best_recog ibest_writer = writer[f"{n}best_recog"] # Write the result to each file ibest_writer["token"][key] = " ".join(token) ibest_writer["token_int"][key] = " ".join(map(str, token_int)) ibest_writer["score"][key] = str(hyp.score) if text is not None: ibest_writer["text"][key] = text
[docs]def get_parser(): parser = config_argparse.ArgumentParser( description="LM Decoding (conditional generation)", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) # Note(kamo): Use '_' instead of '-' as separator. # '-' is confusing if written in yaml. parser.add_argument( "--log_level", type=lambda x: x.upper(), default="INFO", choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"), help="The verbose level of logging", ) parser.add_argument("--output_dir", type=str, required=True) parser.add_argument( "--ngpu", type=int, default=0, help="The number of gpus. 0 indicates CPU mode", ) parser.add_argument("--seed", type=int, default=0, help="Random seed") parser.add_argument( "--dtype", default="float32", choices=["float16", "float32", "float64"], help="Data type", ) parser.add_argument( "--num_workers", type=int, default=1, help="The number of workers used for DataLoader", ) group = parser.add_argument_group("Input data related") group.add_argument( "--data_path_and_name_and_type", type=str2triple_str, required=True, action="append", ) group.add_argument("--key_file", type=str_or_none) group.add_argument("--allow_variable_data_keys", type=str2bool, default=False) group = parser.add_argument_group("The model configuration related") group.add_argument( "--lm_train_config", type=str, help="LM training configuration", ) group.add_argument( "--lm_file", type=str, help="LM parameter file", ) group.add_argument( "--word_lm_train_config", type=str, help="Word LM training configuration", ) group.add_argument( "--word_lm_file", type=str, help="Word LM parameter file", ) group.add_argument( "--ngram_file", type=str, help="N-gram parameter file", ) group.add_argument( "--model_tag", type=str, help="Pretrained model tag. If specify this option, *_train_config and " "*_file will be overwritten", ) group = parser.add_argument_group("Quantization related") group.add_argument( "--quantize_lm", type=str2bool, default=False, help="Apply dynamic quantization to LM.", ) group.add_argument( "--quantize_modules", type=str, nargs="*", default=["Linear"], help="""List of modules to be dynamically quantized. E.g.: --quantize_modules=[Linear,LSTM,GRU]. Each specified module should be an attribute of 'torch.nn', e.g.: torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""", ) group.add_argument( "--quantize_dtype", type=str, default="qint8", choices=["float16", "qint8"], help="Dtype for dynamic quantization.", ) group = parser.add_argument_group("Beam-search related") group.add_argument( "--batch_size", type=int, default=1, help="Batch size for inference", ) group.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses") group.add_argument("--beam_size", type=int, default=20, help="Beam size") group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty") group.add_argument( "--maxlen", type=int, default=100, help="Maximum output length", ) group.add_argument( "--minlen", type=int, default=1, help="Minimum output length", ) group.add_argument("--ngram_weight", type=float, default=0.0, help="ngram weight") group = parser.add_argument_group("Text converter related") group.add_argument( "--token_type", type=str_or_none, default=None, choices=["char", "word", "bpe", None], help="Token type for LM. If not given, refers from the train args", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="Model path for sentencepiece. If not given, refers from the train args", ) return parser
[docs]def main(cmd=None): print(get_commandline_args(), file=sys.stderr) parser = get_parser() args = parser.parse_args(cmd) kwargs = vars(args) kwargs.pop("config", None) inference(**kwargs)
if __name__ == "__main__": main()