mirror of
https://github.com/SillyTavern/SillyTavern-Extras.git
synced 2026-03-02 01:50:02 +00:00
140 lines
4.2 KiB
Python
140 lines
4.2 KiB
Python
# 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 numpy as np
|
|
|
|
from fairseq.data import BaseWrapperDataset, plasma_utils
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class ResamplingDataset(BaseWrapperDataset):
|
|
"""Randomly samples from a given dataset at each epoch.
|
|
|
|
Sampling is done with or without replacement, depending on the "replace"
|
|
parameter.
|
|
|
|
Optionally, the epoch size can be rescaled. This is potentially desirable
|
|
to increase per-epoch coverage of the base dataset (since sampling with
|
|
replacement means that many items in the dataset will be left out). In the
|
|
case of sampling without replacement, size_ratio should be strictly less
|
|
than 1.
|
|
|
|
Args:
|
|
dataset (~torch.utils.data.Dataset): dataset on which to sample.
|
|
weights (List[float]): list of probability weights
|
|
(default: None, which corresponds to uniform sampling).
|
|
replace (bool): sampling mode; True for "with replacement", or False
|
|
for "without replacement" (default: True)
|
|
size_ratio (float): the ratio to subsample to; must be positive
|
|
(default: 1.0).
|
|
batch_by_size (bool): whether or not to batch by sequence length
|
|
(default: True).
|
|
seed (int): RNG seed to use (default: 0).
|
|
epoch (int): starting epoch number (default: 1).
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
dataset,
|
|
weights=None,
|
|
replace=True,
|
|
size_ratio=1.0,
|
|
batch_by_size=True,
|
|
seed=0,
|
|
epoch=1,
|
|
):
|
|
super().__init__(dataset)
|
|
|
|
if weights is None:
|
|
self.weights = None
|
|
|
|
else:
|
|
assert len(weights) == len(dataset)
|
|
weights_arr = np.array(weights, dtype=np.float64)
|
|
weights_arr /= weights_arr.sum()
|
|
self.weights = plasma_utils.PlasmaArray(weights_arr)
|
|
|
|
self.replace = replace
|
|
|
|
assert size_ratio > 0.0
|
|
if not self.replace:
|
|
assert size_ratio < 1.0
|
|
self.size_ratio = float(size_ratio)
|
|
self.actual_size = np.ceil(len(dataset) * self.size_ratio).astype(int)
|
|
|
|
self.batch_by_size = batch_by_size
|
|
self.seed = seed
|
|
|
|
self._cur_epoch = None
|
|
self._cur_indices = None
|
|
|
|
self.set_epoch(epoch)
|
|
|
|
def __getitem__(self, index):
|
|
return self.dataset[self._cur_indices.array[index]]
|
|
|
|
def __len__(self):
|
|
return self.actual_size
|
|
|
|
@property
|
|
def sizes(self):
|
|
if isinstance(self.dataset.sizes, list):
|
|
return [s[self._cur_indices.array] for s in self.dataset.sizes]
|
|
return self.dataset.sizes[self._cur_indices.array]
|
|
|
|
def num_tokens(self, index):
|
|
return self.dataset.num_tokens(self._cur_indices.array[index])
|
|
|
|
def size(self, index):
|
|
return self.dataset.size(self._cur_indices.array[index])
|
|
|
|
def ordered_indices(self):
|
|
if self.batch_by_size:
|
|
order = [
|
|
np.arange(len(self)),
|
|
self.sizes,
|
|
] # No need to handle `self.shuffle == True`
|
|
return np.lexsort(order)
|
|
else:
|
|
return np.arange(len(self))
|
|
|
|
def prefetch(self, indices):
|
|
self.dataset.prefetch(self._cur_indices.array[indices])
|
|
|
|
@property
|
|
def can_reuse_epoch_itr_across_epochs(self):
|
|
return False
|
|
|
|
def set_epoch(self, epoch):
|
|
logger.debug("ResamplingDataset.set_epoch: {}".format(epoch))
|
|
super().set_epoch(epoch)
|
|
|
|
if epoch == self._cur_epoch:
|
|
return
|
|
|
|
self._cur_epoch = epoch
|
|
|
|
# Generate a weighted sample of indices as a function of the
|
|
# random seed and the current epoch.
|
|
|
|
rng = np.random.RandomState(
|
|
[
|
|
42, # magic number
|
|
self.seed % (2**32), # global seed
|
|
self._cur_epoch, # epoch index
|
|
]
|
|
)
|
|
self._cur_indices = plasma_utils.PlasmaArray(
|
|
rng.choice(
|
|
len(self.dataset),
|
|
self.actual_size,
|
|
replace=self.replace,
|
|
p=(None if self.weights is None else self.weights.array),
|
|
)
|
|
)
|