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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 datetime
import hashlib
import logging
import time
from bisect import bisect_right
from collections import OrderedDict, defaultdict
from enum import Enum
from typing import List
import numpy as np
import torch
from fairseq.data import FairseqDataset, data_utils
from fairseq.distributed import utils as distributed_utils
def get_time_gap(s, e):
return (
datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s)
).__str__()
logger = logging.getLogger(__name__)
def default_virtual_size_func(datasets, ratios, max_scale_up=1.5):
sizes = [len(d) for d in datasets]
if ratios is None:
return sum(sizes)
largest_idx = np.argmax(sizes)
largest_r = ratios[largest_idx]
largest_s = sizes[largest_idx]
# set virtual sizes relative to the largest dataset
virtual_sizes = [(r / largest_r) * largest_s for r in ratios]
vsize = sum(virtual_sizes)
max_size = sum(sizes) * max_scale_up
return int(vsize if vsize < max_size else max_size)
class CollateFormat(Enum):
single = 1
ordered_dict = 2
class SampledMultiDataset(FairseqDataset):
"""Samples from multiple sub-datasets according to given sampling ratios.
Args:
datasets (
List[~torch.utils.data.Dataset]
or OrderedDict[str, ~torch.utils.data.Dataset]
): datasets
sampling_ratios (List[float]): list of probability of each dataset to be sampled
(default: None, which corresponds to concatenating all dataset together).
seed (int): RNG seed to use (default: 2).
epoch (int): starting epoch number (default: 1).
eval_key (str, optional): a key used at evaluation time that causes
this instance to pass-through batches from *datasets[eval_key]*.
collate_format (CollateFormat): collater output format, either CollateFormat.ordered_dict or
CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures
the collater to output batches of data mixed from all sub-datasets,
and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys
of sub-datasets.
Note that not all sub-datasets will present in a single batch in both formats.
virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func).
split (str): the split of the data, e.g. 'train', 'valid' or 'test'.
shared_collater (bool): whether or not to all sub-datasets have the same collater.
shuffle (bool): whether or not to shuffle data (default: True).
"""
def __init__(
self,
datasets,
sampling_ratios=None,
seed=2,
epoch=1,
eval_key=None,
collate_format=CollateFormat.single,
virtual_size=default_virtual_size_func,
split="",
shared_collater=False,
shuffle=True,
):
super().__init__()
self.shared_collater = shared_collater
self.shuffle = shuffle
if isinstance(datasets, OrderedDict):
self.keys = list(datasets.keys())
datasets = list(datasets.values())
elif isinstance(datasets, List):
self.keys = list(range(len(datasets)))
else:
raise AssertionError()
self.datasets = datasets
self.split = split
self.eval_key = eval_key
if self.eval_key is not None:
self.collate_format = CollateFormat.single
else:
self.collate_format = collate_format
self.seed = seed
self._cur_epoch = None
self.cumulated_sizes = None
# self.datasets[k][self._cur_indices[i]] is the data item i in this sampled dataset
# namely, data item i is sampled from the kth sub-dataset self.datasets[k]
# where self.cumulated_sizes[k-1] <= i < self.cumulated_sizes[k]
self._cur_indices = None
self._sizes = None
self.virtual_size_per_dataset = None
# caching properties
self._reset_cached_properties()
self.setup_sampling(sampling_ratios, virtual_size)
self.set_epoch(epoch)
def _clean_if_not_none(self, var_list):
for v in var_list:
if v is not None:
del v
def _reset_cached_properties(self):
self._clean_if_not_none([self._sizes, self._cur_indices])
self._sizes = None
self._cur_indices = None
def setup_sampling(self, sample_ratios, virtual_size):
sizes = [len(d) for d in self.datasets]
if sample_ratios is None:
# default back to concating datasets
self.sample_ratios = None
self.virtual_size = sum(sizes)
else:
if not isinstance(sample_ratios, np.ndarray):
sample_ratios = np.array(sample_ratios)
self.sample_ratios = sample_ratios
virtual_size = (
default_virtual_size_func if virtual_size is None else virtual_size
)
self.virtual_size = (
virtual_size(self.datasets, self.sample_ratios)
if callable(virtual_size)
else virtual_size
)
def adjust_sampling(self, epoch, sampling_ratios, virtual_size):
if sampling_ratios is not None:
sampling_ratios = self._sync_sample_ratios(sampling_ratios)
self.setup_sampling(sampling_ratios, virtual_size)
def _sync_sample_ratios(self, ratios):
# in case the ratios are not precisely the same across processes
# also to ensure every procresses update the ratios in the same pace
ratios = torch.DoubleTensor(ratios)
if torch.distributed.is_initialized():
if torch.cuda.is_available():
distributed_utils.all_reduce(
ratios.cuda(), group=distributed_utils.get_data_parallel_group()
)
else:
distributed_utils.all_reduce(
ratios, group=distributed_utils.get_data_parallel_group()
)
ret = ratios.cpu()
ret = ret.numpy()
return ret
def random_choice_in_dataset(self, rng, dataset, choice_size):
if hasattr(dataset, "random_choice_in_dataset"):
return dataset.random_choice_in_dataset(rng, choice_size)
dataset_size = len(dataset)
return rng.choice(
dataset_size, choice_size, replace=(choice_size > dataset_size)
)
def get_virtual_indices(self, rng, datasets, sample_ratios, virtual_size):
def get_counts(sample_ratios):
counts = np.array([virtual_size * r for r in sample_ratios], dtype=np.int64)
diff = virtual_size - counts.sum()
assert diff >= 0
# due to round-offs, the size might not match the desired sizes
if diff > 0:
dataset_indices = rng.choice(
len(sample_ratios), size=diff, p=sample_ratios
)
for i in dataset_indices:
counts[i] += 1
return counts
def get_in_dataset_indices(datasets, sizes, sample_ratios):
counts = get_counts(sample_ratios)
# uniformally sample desired counts for each dataset
# if the desired counts are large, sample with replacement:
indices = [
self.random_choice_in_dataset(rng, d, c)
for c, d in zip(counts, datasets)
]
return indices
sizes = [len(d) for d in datasets]
if sample_ratios is None:
# default back to concating datasets
in_dataset_indices = [list(range(s)) for s in sizes]
virtual_sizes_per_dataset = sizes
else:
ratios = sample_ratios / sample_ratios.sum()
in_dataset_indices = get_in_dataset_indices(datasets, sizes, ratios)
virtual_sizes_per_dataset = [len(d) for d in in_dataset_indices]
virtual_sizes_per_dataset = np.array(virtual_sizes_per_dataset, np.int64)
cumulative_sizes = np.cumsum(virtual_sizes_per_dataset)
assert sum(virtual_sizes_per_dataset) == virtual_size
assert cumulative_sizes[-1] == virtual_size
if virtual_size < sum(sizes):
logger.warning(
f"virtual data size ({virtual_size}) is less than real data size ({sum(sizes)})."
" If virtual size << real data size, there could be data coverage issue."
)
in_dataset_indices = np.hstack(in_dataset_indices)
return in_dataset_indices, cumulative_sizes, virtual_sizes_per_dataset
def _get_dataset_and_index(self, index):
i = bisect_right(self.cumulated_sizes, index)
return i, self._cur_indices[index]
def __getitem__(self, index):
# self.__getitem__(index) returns self.datasets[k][self._cur_indices[index]]
# where k satisfies self.cumulated_sizes[k - 1] <= k < self.cumulated_sizes[k]
ds_idx, ds_sample_idx = self._get_dataset_and_index(index)
ret = (ds_idx, self.datasets[ds_idx][ds_sample_idx])
return ret
def num_tokens(self, index):
return self.sizes[index].max()
def num_tokens_vec(self, indices):
sizes_vec = self.sizes[np.array(indices)]
# max across all dimensions but first one
return np.amax(sizes_vec, axis=tuple(range(1, len(sizes_vec.shape))))
def size(self, index):
return self.sizes[index]
def __len__(self):
return self.virtual_size
def collater(self, samples, **extra_args):
"""Merge a list of samples to form a mini-batch."""
if len(samples) == 0:
return None
if self.collate_format == "ordered_dict":
collect_samples = [[] for _ in range(len(self.datasets))]
for (i, sample) in samples:
collect_samples[i].append(sample)
batch = OrderedDict(
[
(self.keys[i], dataset.collater(collect_samples[i]))
for i, (key, dataset) in enumerate(zip(self.keys, self.datasets))
if len(collect_samples[i]) > 0
]
)
elif self.shared_collater:
batch = self.datasets[0].collater([s for _, s in samples])
else:
samples_dict = defaultdict(list)
pad_to_length = (
defaultdict(int)
if "pad_to_length" not in extra_args
else extra_args["pad_to_length"]
)
for ds_idx, s in samples:
pad_to_length["source"] = max(
pad_to_length["source"], s["source"].size(0)
)
if s["target"] is not None:
pad_to_length["target"] = max(
pad_to_length["target"], s["target"].size(0)
)
samples_dict[ds_idx].append(s)
batches = [
self.datasets[i].collater(samples_dict[i], pad_to_length=pad_to_length)
for i in range(len(self.datasets))
if len(samples_dict[i]) > 0
]
def straight_data(tensors):
batch = torch.cat(tensors, dim=0)
return batch
src_lengths = straight_data(
[b["net_input"]["src_lengths"] for b in batches]
)
src_lengths, sort_order = src_lengths.sort(descending=True)
def straight_order(tensors):
batch = straight_data(tensors)
return batch.index_select(0, sort_order)
batch = {
"id": straight_order([b["id"] for b in batches]),
"nsentences": sum(b["nsentences"] for b in batches),
"ntokens": sum(b["ntokens"] for b in batches),
"net_input": {
"src_tokens": straight_order(
[b["net_input"]["src_tokens"] for b in batches]
),
"src_lengths": src_lengths,
},
"target": straight_order([b["target"] for b in batches])
if batches[0]["target"] is not None
else None,
}
if "prev_output_tokens" in batches[0]["net_input"]:
batch["net_input"]["prev_output_tokens"] = straight_order(
[b["net_input"]["prev_output_tokens"] for b in batches]
)
if "src_lang_id" in batches[0]["net_input"]:
batch["net_input"]["src_lang_id"] = straight_order(
[b["net_input"]["src_lang_id"] for b in batches]
)
if "tgt_lang_id" in batches[0]:
batch["tgt_lang_id"] = straight_order(
[b["tgt_lang_id"] for b in batches]
)
return batch
@property
def sizes(self):
if self._sizes is not None:
return self._sizes
start_time = time.time()
in_sub_dataset_indices = [
self._cur_indices[
0 if i == 0 else self.cumulated_sizes[i - 1] : self.cumulated_sizes[i]
]
for i in range(len(self.datasets))
]
sub_dataset_sizes = [
d.sizes[indices]
for d, indices in zip(self.datasets, in_sub_dataset_indices)
]
self._sizes = np.vstack(sub_dataset_sizes)
logger.info(f"sizes() calling time: {get_time_gap(start_time, time.time())}")
return self._sizes
def ordered_indices(self):
if self.shuffle:
indices = np.random.permutation(len(self))
else:
indices = np.arange(len(self))
sizes = self.sizes
tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
src_sizes = (
sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
)
# sort by target length, then source length
if tgt_sizes is not None:
indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]
sort_indices = indices[np.argsort(src_sizes[indices], kind="mergesort")]
return sort_indices
def prefetch(self, indices):
prefetch_indices = [[] for _ in range(len(self.datasets))]
for i in indices:
ds_idx, ds_sample_idx = self._get_dataset_and_index(i)
prefetch_indices[ds_idx].append(ds_sample_idx)
for i in range(len(prefetch_indices)):
self.datasets[i].prefetch(prefetch_indices[i])
@property
def can_reuse_epoch_itr_across_epochs(self):
return False
def set_epoch(self, epoch):
super().set_epoch(epoch)
if epoch == self._cur_epoch:
# re-enter so return
return
for d in self.datasets:
if hasattr(d, "set_epoch"):
d.set_epoch(epoch)
self._cur_epoch = epoch
self._establish_virtual_datasets()
def _establish_virtual_datasets(self):
if self.sample_ratios is None and self._cur_indices is not None:
# not a samping dataset, no need to resample if indices are already established
return
self._reset_cached_properties()
start_time = time.time()
# Generate a weighted sample of indices as a function of the
# random seed and the current epoch.
rng = np.random.RandomState(
[
int(
hashlib.sha1(
str(self.__class__.__name__).encode("utf-8")
).hexdigest(),
16,
)
% (2**32),
self.seed % (2**32), # global seed
self._cur_epoch, # epoch index,
]
)
self._clean_if_not_none(
[self.cumulated_sizes, self.virtual_size_per_dataset, self._sizes]
)
self._sizes = None
indices, cumulated_sizes, virtual_size_per_dataset = self.get_virtual_indices(
rng, self.datasets, self.sample_ratios, self.virtual_size
)
self._cur_indices = indices
self.cumulated_sizes = cumulated_sizes
self.virtual_size_per_dataset = virtual_size_per_dataset
raw_sizes = [len(d) for d in self.datasets]
sampled_sizes = self.virtual_size_per_dataset
logger.info(
f"[{self.split}] Raw sizes: {str(dict(zip(self.keys, raw_sizes)))}; "
f"raw total size: {sum(raw_sizes)}"
)
logger.info(
f"[{self.split}] Resampled sizes: {str(dict(zip(self.keys, sampled_sizes)))}; "
f"resampled total size: {sum(sampled_sizes)}"
)
if self.sample_ratios is not None:
logger.info(
f"[{self.split}] Upsampling ratios: {str(dict(zip(self.keys, self.sample_ratios)))}"
)
else:
logger.info(f"[{self.split}] A concat dataset")
logger.info(
f"[{self.split}] virtual dataset established time: {get_time_gap(start_time, time.time())}"
)
def filter_indices_by_size(self, indices, max_sizes):
"""Filter a list of sample indices. Remove those that are longer
than specified in max_sizes.
Args:
indices (np.array): original array of sample indices
max_sizes (int or list[int] or tuple[int]): max sample size,
can be defined separately for src and tgt (then list or tuple)
Returns:
np.array: filtered sample array
list: list of removed indices
"""
sizes = self.sizes
tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
src_sizes = (
sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
)
return data_utils.filter_paired_dataset_indices_by_size(
src_sizes, tgt_sizes, indices, max_sizes
)
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