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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import numpy as np
from fairseq import utils
from fairseq.data import (
ConcatSentencesDataset,
Dictionary,
IdDataset,
NestedDictionaryDataset,
NumelDataset,
NumSamplesDataset,
PrependTokenDataset,
RawLabelDataset,
RightPadDataset,
SortDataset,
TruncateDataset,
data_utils,
)
from fairseq.data.shorten_dataset import maybe_shorten_dataset
from fairseq.tasks import LegacyFairseqTask, register_task
logger = logging.getLogger(__name__)
@register_task("sentence_ranking")
class SentenceRankingTask(LegacyFairseqTask):
"""
Ranking task on multiple sentences.
Args:
dictionary (Dictionary): the dictionary for the input of the task
"""
@staticmethod
def add_args(parser):
"""Add task-specific arguments to the parser."""
parser.add_argument("data", metavar="FILE", help="file prefix for data")
parser.add_argument(
"--num-classes", type=int, help="number of sentences to be ranked"
)
parser.add_argument(
"--init-token",
type=int,
help="add token at the beginning of each batch item",
)
parser.add_argument(
"--separator-token", type=int, help="add separator token between inputs"
)
parser.add_argument("--no-shuffle", action="store_true")
parser.add_argument(
"--shorten-method",
default="none",
choices=["none", "truncate", "random_crop"],
help="if not none, shorten sequences that exceed --tokens-per-sample",
)
parser.add_argument(
"--shorten-data-split-list",
default="",
help="comma-separated list of dataset splits to apply shortening to, "
'e.g., "train,valid" (default: all dataset splits)',
)
parser.add_argument(
"--max-option-length", type=int, help="max length for each option"
)
def __init__(self, args, dictionary):
super().__init__(args)
self.dictionary = dictionary
@classmethod
def load_dictionary(cls, args, filename, source=True):
"""Load the dictionary from the filename
Args:
filename (str): the filename
"""
dictionary = Dictionary.load(filename)
dictionary.add_symbol("<mask>")
return dictionary
@classmethod
def setup_task(cls, args, **kwargs):
assert (
args.criterion == "sentence_ranking"
), "Must set --criterion=sentence_ranking"
# load data dictionary
data_dict = cls.load_dictionary(
args,
os.path.join(args.data, "input0", "dict.txt"),
source=True,
)
logger.info("[input] dictionary: {} types".format(len(data_dict)))
return SentenceRankingTask(args, data_dict)
def load_dataset(self, split, combine=False, **kwargs):
"""Load a given dataset split (e.g., train, valid, test)."""
def get_path(type, split):
return os.path.join(self.args.data, type, split)
def make_dataset(type, dictionary):
split_path = get_path(type, split)
dataset = data_utils.load_indexed_dataset(
split_path,
self.source_dictionary,
self.args.dataset_impl,
combine=combine,
)
return dataset
input0 = make_dataset("input0", self.source_dictionary)
input_options = [
make_dataset("input{idx}".format(idx=idx + 1), self.source_dictionary)
for idx in range(self.args.num_classes)
]
if self.args.separator_token is not None:
input0 = PrependTokenDataset(input0, self.args.separator_token)
src_tokens = []
for input_option in input_options:
if self.args.init_token is not None:
input_option = PrependTokenDataset(input_option, self.args.init_token)
if self.args.max_option_length is not None:
input_option = TruncateDataset(
input_option, self.args.max_option_length
)
src_token = ConcatSentencesDataset(input_option, input0)
src_token = maybe_shorten_dataset(
src_token,
split,
self.args.shorten_data_split_list,
self.args.shorten_method,
self.args.max_positions,
self.args.seed,
)
src_tokens.append(src_token)
with data_utils.numpy_seed(self.args.seed):
shuffle = np.random.permutation(len(src_tokens[0]))
dataset = {
"id": IdDataset(),
"nsentences": NumSamplesDataset(),
"ntokens": NumelDataset(src_tokens[0], reduce=True),
}
for src_token_idx in range(len(src_tokens)):
dataset.update(
{
"net_input{idx}".format(idx=src_token_idx + 1): {
"src_tokens": RightPadDataset(
src_tokens[src_token_idx],
pad_idx=self.source_dictionary.pad(),
),
"src_lengths": NumelDataset(
src_tokens[src_token_idx], reduce=False
),
}
}
)
label_path = "{}.label".format(get_path("label", split))
if os.path.exists(label_path):
with open(label_path) as h:
dataset.update(
target=RawLabelDataset([int(x.strip()) for x in h.readlines()])
)
nested_dataset = NestedDictionaryDataset(
dataset,
sizes=[np.maximum.reduce([src_token.sizes for src_token in src_tokens])],
)
if self.args.no_shuffle:
dataset = nested_dataset
else:
dataset = SortDataset(
nested_dataset,
# shuffle
sort_order=[shuffle],
)
logger.info("Loaded {0} with #samples: {1}".format(split, len(dataset)))
self.datasets[split] = dataset
return self.datasets[split]
def build_model(self, args, from_checkpoint=False):
from fairseq import models
model = models.build_model(args, self, from_checkpoint)
model.register_classification_head(
getattr(args, "ranking_head_name", "sentence_classification_head"),
num_classes=1,
)
return model
def max_positions(self):
return self.args.max_positions
@property
def source_dictionary(self):
return self.dictionary
@property
def target_dictionary(self):
return self.dictionary
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