#!/usr/bin/env python3
import argparse
import logging
import sys
from pathlib import Path
from typing import Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch.nn.parallel import data_parallel
from typeguard import check_argument_types
from espnet2.fileio.datadir_writer import DatadirWriter
from espnet2.tasks.lm import LMTask
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.forward_adaptor import ForwardAdaptor
from espnet2.torch_utils.set_all_random_seed import set_all_random_seed
from espnet2.utils import config_argparse
from espnet2.utils.types import float_or_none, str2bool, str2triple_str, str_or_none
from espnet.utils.cli_utils import get_commandline_args
[docs]def calc_perplexity(
output_dir: str,
batch_size: int,
dtype: str,
ngpu: int,
seed: int,
num_workers: int,
log_level: Union[int, str],
data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
key_file: Optional[str],
train_config: Optional[str],
model_file: Optional[str],
log_base: Optional[float],
allow_variable_data_keys: bool,
):
assert check_argument_types()
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 LM
model, train_args = LMTask.build_model_from_file(train_config, model_file, device)
# Wrape model to make model.nll() data-parallel
wrapped_model = ForwardAdaptor(model, "nll")
wrapped_model.to(dtype=getattr(torch, dtype)).eval()
logging.info(f"Model:\n{model}")
# 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(train_args, False),
collate_fn=LMTask.build_collate_fn(train_args, False),
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
# 4. Start for-loop
with DatadirWriter(output_dir) as writer:
total_nll = 0.0
total_ntokens = 0
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}"
with torch.no_grad():
batch = to_device(batch, device)
if ngpu <= 1:
# NOTE(kamo): data_parallel also should work with ngpu=1,
# but for debuggability it's better to keep this block.
nll, lengths = wrapped_model(**batch)
else:
nll, lengths = data_parallel(
wrapped_model, (), range(ngpu), module_kwargs=batch
)
assert _bs == len(nll) == len(lengths), (_bs, len(nll), len(lengths))
# nll: (B, L) -> (B,)
nll = nll.detach().cpu().numpy().sum(1)
# lengths: (B,)
lengths = lengths.detach().cpu().numpy()
total_nll += nll.sum()
total_ntokens += lengths.sum()
for key, _nll, ntoken in zip(keys, nll, lengths):
if log_base is None:
utt_ppl = np.exp(_nll / ntoken)
else:
utt_ppl = log_base ** (_nll / ntoken / np.log(log_base))
# Write PPL of each utts for debugging or analysis
writer["utt2ppl"][key] = str(utt_ppl)
writer["utt2ntokens"][key] = str(ntoken)
if log_base is None:
ppl = np.exp(total_nll / total_ntokens)
else:
ppl = log_base ** (total_nll / total_ntokens / np.log(log_base))
with (Path(output_dir) / "ppl").open("w", encoding="utf-8") as f:
f.write(f"{ppl}\n")
with (Path(output_dir) / "base").open("w", encoding="utf-8") as f:
if log_base is None:
_log_base = np.e
else:
_log_base = log_base
f.write(f"{_log_base}\n")
logging.info(f"PPL={ppl}")
[docs]def get_parser():
parser = config_argparse.ArgumentParser(
description="Calc perplexity",
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",
)
parser.add_argument(
"--batch_size",
type=int,
default=1,
help="The batch size for inference",
)
parser.add_argument(
"--log_base",
type=float_or_none,
default=None,
help="The base of logarithm for Perplexity. "
"If None, napier's constant is used.",
)
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("--train_config", type=str)
group.add_argument("--model_file", type=str)
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)
calc_perplexity(**kwargs)
if __name__ == "__main__":
main()