# -*- encoding: utf-8 -*-
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#!/usr/bin/env python3
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import argparse
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import logging
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import os
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import sys
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from typing import Union, Dict, Any
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.utils.types import float_or_none
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import argparse
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import logging
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from pathlib import Path
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import sys
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import os
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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from typing import Dict
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from typing import Any
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from typing import List
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import numpy as np
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import torch
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from torch.nn.parallel import data_parallel
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from typeguard import check_argument_types
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from funasr.tasks.lm import LMTask
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from funasr.datasets.preprocessor import LMPreprocessor
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.forward_adaptor import ForwardAdaptor
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.types import float_or_none
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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def inference_lm(
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batch_size: int,
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dtype: str,
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ngpu: int,
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seed: int,
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num_workers: int,
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log_level: Union[int, str],
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key_file: Optional[str],
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train_config: Optional[str],
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model_file: Optional[str],
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log_base: Optional[float] = 10,
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allow_variable_data_keys: bool = False,
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split_with_space: Optional[bool] = False,
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seg_dict_file: Optional[str] = None,
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output_dir: Optional[str] = None,
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param_dict: dict = None,
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**kwargs,
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):
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assert check_argument_types()
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ncpu = kwargs.get("ncpu", 1)
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torch.set_num_threads(ncpu)
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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# 1. Set random-seed
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set_all_random_seed(seed)
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# 2. Build Model
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model, train_args = LMTask.build_model_from_file(
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train_config, model_file, device)
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wrapped_model = ForwardAdaptor(model, "nll")
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wrapped_model.to(dtype=getattr(torch, dtype)).to(device=device).eval()
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logging.info(f"Model:\n{model}")
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preprocessor = LMPreprocessor(
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train=False,
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token_type=train_args.token_type,
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token_list=train_args.token_list,
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bpemodel=train_args.bpemodel,
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text_cleaner=train_args.cleaner,
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g2p_type=train_args.g2p,
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text_name="text",
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non_linguistic_symbols=train_args.non_linguistic_symbols,
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split_with_space=split_with_space,
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seg_dict_file=seg_dict_file
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)
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def _forward(
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data_path_and_name_and_type,
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raw_inputs: Union[List[Any], bytes, str] = None,
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output_dir_v2: Optional[str] = None,
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param_dict: dict = None,
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):
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results = []
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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if output_path is not None:
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writer = DatadirWriter(output_path)
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else:
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writer = None
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if raw_inputs != None:
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line = raw_inputs.strip()
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key = "lm demo"
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if line == "":
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item = {'key': key, 'value': ""}
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results.append(item)
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return results
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batch = {}
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batch['text'] = line
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if preprocessor != None:
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batch = preprocessor(key, batch)
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# Force data-precision
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for name in batch:
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value = batch[name]
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if not isinstance(value, np.ndarray):
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raise RuntimeError(
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f"All values must be converted to np.ndarray object "
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f'by preprocessing, but "{name}" is still {type(value)}.'
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)
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# Cast to desired type
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if value.dtype.kind == "f":
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value = value.astype("float32")
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elif value.dtype.kind == "i":
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value = value.astype("long")
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else:
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raise NotImplementedError(f"Not supported dtype: {value.dtype}")
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batch[name] = value
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batch["text_lengths"] = torch.from_numpy(
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np.array([len(batch["text"])], dtype='int32'))
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batch["text"] = np.expand_dims(batch["text"], axis=0)
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with torch.no_grad():
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batch = to_device(batch, device)
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if ngpu <= 1:
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nll, lengths = wrapped_model(**batch)
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else:
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nll, lengths = data_parallel(
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wrapped_model, (), range(ngpu), module_kwargs=batch
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)
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## compute ppl
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ppl_out_batch = ""
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ids2tokens = preprocessor.token_id_converter.ids2tokens
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for sent_ids, sent_nll in zip(batch['text'], nll):
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pre_word = "<s>"
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cur_word = None
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sent_lst = ids2tokens(sent_ids) + ['</s>']
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ppl_out = " ".join(sent_lst) + "\n"
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for word, word_nll in zip(sent_lst, sent_nll):
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cur_word = word
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word_nll = -word_nll.cpu()
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if log_base is None:
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word_prob = np.exp(word_nll)
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else:
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word_prob = log_base ** (word_nll / np.log(log_base))
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ppl_out += ' p( {cur} | {pre} ) = {prob} [ {word_nll} ]\n'.format(
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cur=cur_word,
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pre=pre_word,
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prob=round(word_prob.item(), 8),
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word_nll=round(word_nll.item(), 8)
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)
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pre_word = cur_word
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sent_nll_mean = sent_nll.mean().cpu().numpy()
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sent_nll_sum = sent_nll.sum().cpu().numpy()
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if log_base is None:
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sent_ppl = np.exp(sent_nll_mean)
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else:
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sent_ppl = log_base ** (sent_nll_mean / np.log(log_base))
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ppl_out += 'logprob= {sent_nll} ppl= {sent_ppl}\n\n'.format(
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sent_nll=round(-sent_nll_sum.item(), 4),
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sent_ppl=round(sent_ppl.item(), 4)
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)
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ppl_out_batch += ppl_out
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item = {'key': key, 'value': ppl_out}
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if writer is not None:
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writer["ppl"][key + ":\n"] = ppl_out
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results.append(item)
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return results
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# 3. Build data-iterator
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loader = LMTask.build_streaming_iterator(
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data_path_and_name_and_type,
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dtype=dtype,
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batch_size=batch_size,
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key_file=key_file,
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num_workers=num_workers,
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preprocess_fn=preprocessor,
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collate_fn=LMTask.build_collate_fn(train_args, False),
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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)
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# 4. Start for-loop
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total_nll = 0.0
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total_ntokens = 0
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ppl_out_all = ""
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for keys, batch in loader:
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assert isinstance(batch, dict), type(batch)
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assert all(isinstance(s, str) for s in keys), keys
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_bs = len(next(iter(batch.values())))
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assert len(keys) == _bs, f"{len(keys)} != {_bs}"
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ppl_out_batch = ""
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with torch.no_grad():
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batch = to_device(batch, device)
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if ngpu <= 1:
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# NOTE(kamo): data_parallel also should work with ngpu=1,
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# but for debuggability it's better to keep this block.
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nll, lengths = wrapped_model(**batch)
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else:
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nll, lengths = data_parallel(
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wrapped_model, (), range(ngpu), module_kwargs=batch
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)
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## print ppl
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ids2tokens = preprocessor.token_id_converter.ids2tokens
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for key, sent_ids, sent_nll in zip(keys, batch['text'], nll):
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pre_word = "<s>"
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cur_word = None
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sent_lst = ids2tokens(sent_ids) + ['</s>']
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ppl_out = " ".join(sent_lst) + "\n"
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for word, word_nll in zip(sent_lst, sent_nll):
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cur_word = word
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word_nll = -word_nll.cpu()
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if log_base is None:
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word_prob = np.exp(word_nll)
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else:
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word_prob = log_base ** (word_nll / np.log(log_base))
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ppl_out += ' p( {cur} | {pre} ) = {prob} [ {word_nll} ]\n'.format(
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cur=cur_word,
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pre=pre_word,
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prob=round(word_prob.item(), 8),
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word_nll=round(word_nll.item(), 8)
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)
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pre_word = cur_word
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sent_nll_mean = sent_nll.mean().cpu().numpy()
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sent_nll_sum = sent_nll.sum().cpu().numpy()
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if log_base is None:
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sent_ppl = np.exp(sent_nll_mean)
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else:
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sent_ppl = log_base ** (sent_nll_mean / np.log(log_base))
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ppl_out += 'logprob= {sent_nll} ppl= {sent_ppl}\n\n'.format(
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sent_nll=round(-sent_nll_sum.item(), 4),
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sent_ppl=round(sent_ppl.item(), 4)
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)
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ppl_out_batch += ppl_out
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utt2nll = round(-sent_nll_sum.item(), 5)
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item = {'key': key, 'value': ppl_out}
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if writer is not None:
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writer["ppl"][key + ":\n"] = ppl_out
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writer["utt2nll"][key] = str(utt2nll)
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results.append(item)
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ppl_out_all += ppl_out_batch
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assert _bs == len(nll) == len(lengths), (_bs, len(nll), len(lengths))
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# nll: (B, L) -> (B,)
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nll = nll.detach().cpu().numpy().sum(1)
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# lengths: (B,)
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lengths = lengths.detach().cpu().numpy()
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total_nll += nll.sum()
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total_ntokens += lengths.sum()
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if log_base is None:
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ppl = np.exp(total_nll / total_ntokens)
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else:
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ppl = log_base ** (total_nll / total_ntokens / np.log(log_base))
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avg_ppl = 'logprob= {total_nll} ppl= {total_ppl}\n'.format(
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total_nll=round(-total_nll.item(), 4),
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total_ppl=round(ppl.item(), 4)
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)
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item = {'key': 'AVG PPL', 'value': avg_ppl}
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ppl_out_all += avg_ppl
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if writer is not None:
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writer["ppl"]["AVG PPL : "] = avg_ppl
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results.append(item)
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return results
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return _forward
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def inference_launch(mode, **kwargs):
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if mode == "transformer":
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return inference_lm(**kwargs)
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else:
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logging.info("Unknown decoding mode: {}".format(mode))
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return None
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="Calc perplexity",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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"--log_level",
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type=lambda x: x.upper(),
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default="INFO",
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choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
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help="The verbose level of logging",
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)
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parser.add_argument("--output_dir", type=str, required=True)
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parser.add_argument("--gpuid_list", type=str, required=True)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=0,
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help="The number of gpus. 0 indicates CPU mode",
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)
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument("--njob", type=int, default=1, help="Random seed")
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parser.add_argument(
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"--dtype",
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default="float32",
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choices=["float16", "float32", "float64"],
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help="Data type",
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)
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parser.add_argument(
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"--num_workers",
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type=int,
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default=1,
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help="The number of workers used for DataLoader",
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)
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parser.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="The batch size for inference",
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)
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parser.add_argument(
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"--log_base",
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type=float_or_none,
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default=10,
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help="The base of logarithm for Perplexity. "
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"If None, napier's constant is used.",
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required=False
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)
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group = parser.add_argument_group("Input data related")
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group.add_argument(
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"--data_path_and_name_and_type",
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type=str2triple_str,
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action="append",
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required=False
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)
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group.add_argument(
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"--raw_inputs",
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type=str,
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required=False
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)
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group.add_argument("--key_file", type=str_or_none)
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group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
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group.add_argument("--split_with_space", type=str2bool, default=False)
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group.add_argument("--seg_dict_file", type=str_or_none)
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group = parser.add_argument_group("The model configuration related")
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group.add_argument("--train_config", type=str)
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group.add_argument("--model_file", type=str)
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group.add_argument("--mode", type=str, default="lm")
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return parser
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def main(cmd=None):
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print(get_commandline_args(), file=sys.stderr)
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parser = get_parser()
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args = parser.parse_args(cmd)
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kwargs = vars(args)
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kwargs.pop("config", None)
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# set logging messages
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logging.basicConfig(
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level=args.log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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logging.info("Decoding args: {}".format(kwargs))
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# gpu setting
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if args.ngpu > 0:
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jobid = int(args.output_dir.split(".")[-1])
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gpuid = args.gpuid_list.split(",")[(jobid - 1) // args.njob]
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
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kwargs.pop("gpuid_list", None)
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kwargs.pop("njob", None)
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inference_pipeline = inference_launch(**kwargs)
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return inference_pipeline(kwargs["data_path_and_name_and_type"])
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if __name__ == "__main__":
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main()
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