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# Copyright (c) 2021-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import logging
import math
from typing import List, Optional, NamedTuple
import numpy as np
from fairseq.data.resampling_dataset import ResamplingDataset
import torch
from fairseq.data import (
ConcatDataset,
LanguagePairDataset,
FileAudioDataset,
data_utils,
)
from fairseq.data import FairseqDataset
logger = logging.getLogger(__name__)
class ModalityDatasetItem(NamedTuple):
datasetname: str
dataset: any
max_positions: List[int]
max_tokens: Optional[int] = None
max_sentences: Optional[int] = None
def resampling_dataset_present(ds):
if isinstance(ds, ResamplingDataset):
return True
if isinstance(ds, ConcatDataset):
return any(resampling_dataset_present(d) for d in ds.datasets)
if hasattr(ds, "dataset"):
return resampling_dataset_present(ds.dataset)
return False
# MultiModalityDataset: it concate multiple datasets with different modalities.
# Compared with ConcatDataset it can 1) sample data given the ratios for different datasets
# 2) it adds mode to indicate what type of the data samples come from.
# It will be used with GroupedEpochBatchIterator together to generate mini-batch with samples
# from the same type of dataset
# If only one dataset is used, it will perform like the original dataset with mode added
class MultiModalityDataset(ConcatDataset):
def __init__(self, datasets: List[ModalityDatasetItem]):
id_to_mode = []
dsets = []
max_tokens = []
max_sentences = []
max_positions = []
for dset in datasets:
id_to_mode.append(dset.datasetname)
dsets.append(dset.dataset)
max_tokens.append(dset.max_tokens)
max_positions.append(dset.max_positions)
max_sentences.append(dset.max_sentences)
weights = [1.0 for s in dsets]
super().__init__(dsets, weights)
self.max_tokens = max_tokens
self.max_positions = max_positions
self.max_sentences = max_sentences
self.id_to_mode = id_to_mode
self.raw_sub_batch_samplers = []
self._cur_epoch = 0
def set_epoch(self, epoch):
super().set_epoch(epoch)
self._cur_epoch = epoch
def __getitem__(self, idx):
dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
sample = self.datasets[dataset_idx][sample_idx]
return (dataset_idx, sample)
def collater(self, samples):
if len(samples) == 0:
return {}
dataset_idx = samples[0][0]
# make sure all samples in samples are from same dataset
assert sum([0 if dataset_idx == s[0] else 1 for s in samples]) == 0
samples = self.datasets[dataset_idx].collater([x[1] for x in samples])
# add mode
samples["net_input"]["mode"] = self.id_to_mode[dataset_idx]
return samples
def size(self, index: int):
if len(self.datasets) == 1:
return self.datasets[0].size(index)
return super().size(index)
@property
def sizes(self):
if len(self.datasets) == 1:
return self.datasets[0].sizes
return super().sizes
def ordered_indices(self):
"""
Returns indices sorted by length. So less padding is needed.
"""
if len(self.datasets) == 1:
return self.datasets[0].ordered_indices()
indices_group = []
for d_idx, ds in enumerate(self.datasets):
sample_num = self.cumulative_sizes[d_idx]
if d_idx > 0:
sample_num = sample_num - self.cumulative_sizes[d_idx - 1]
assert sample_num == len(ds)
indices_group.append(ds.ordered_indices())
return indices_group
def get_raw_batch_samplers(self, required_batch_size_multiple, seed):
with data_utils.numpy_seed(seed):
indices = self.ordered_indices()
for i, ds in enumerate(self.datasets):
# If we have ResamplingDataset, the same id can correpond to a different
# sample in the next epoch, so we need to rebuild this at every epoch
if i < len(self.raw_sub_batch_samplers) and not resampling_dataset_present(
ds
):
logger.info(f"dataset {i} is valid and it is not re-sampled")
continue
indices[i] = ds.filter_indices_by_size(
indices[i],
self.max_positions[i],
)[0]
sub_batch_sampler = ds.batch_by_size(
indices[i],
max_tokens=self.max_tokens[i],
max_sentences=self.max_sentences[i],
required_batch_size_multiple=required_batch_size_multiple,
)
if i < len(self.raw_sub_batch_samplers):
self.raw_sub_batch_samplers[i] = sub_batch_sampler
else:
self.raw_sub_batch_samplers.append(sub_batch_sampler)
def get_batch_samplers(self, mult_ratios, required_batch_size_multiple, seed):
self.get_raw_batch_samplers(required_batch_size_multiple, seed)
batch_samplers = []
for i, _ in enumerate(self.datasets):
if i > 0:
sub_batch_sampler = [
[y + self.cumulative_sizes[i - 1] for y in x]
for x in self.raw_sub_batch_samplers[i]
]
else:
sub_batch_sampler = list(self.raw_sub_batch_samplers[i])
smp_r = mult_ratios[i]
if smp_r != 1:
is_increase = "increased" if smp_r > 1 else "decreased"
logger.info(
"number of batch for the dataset {} is {} from {} to {}".format(
self.id_to_mode[i],
is_increase,
len(sub_batch_sampler),
int(len(sub_batch_sampler) * smp_r),
)
)
mul_samplers = []
for _ in range(math.floor(smp_r)):
mul_samplers = mul_samplers + sub_batch_sampler
if math.floor(smp_r) != smp_r:
with data_utils.numpy_seed(seed + self._cur_epoch):
np.random.shuffle(sub_batch_sampler)
smp_num = int(
(smp_r - math.floor(smp_r)) * len(sub_batch_sampler)
)
mul_samplers = mul_samplers + sub_batch_sampler[:smp_num]
sub_batch_sampler = mul_samplers
else:
logger.info(
"dataset {} batch number is {} ".format(
self.id_to_mode[i], len(sub_batch_sampler)
)
)
batch_samplers.append(sub_batch_sampler)
return batch_samplers
class LangPairMaskDataset(FairseqDataset):
def __init__(
self,
dataset: LanguagePairDataset,
src_eos: int,
src_bos: Optional[int] = None,
noise_id: Optional[int] = -1,
mask_ratio: Optional[float] = 0,
mask_type: Optional[str] = "random",
):
self.dataset = dataset
self.src_eos = src_eos
self.src_bos = src_bos
self.noise_id = noise_id
self.mask_ratio = mask_ratio
self.mask_type = mask_type
assert mask_type in ("random", "tail")
@property
def src_sizes(self):
return self.dataset.src_sizes
@property
def tgt_sizes(self):
return self.dataset.tgt_sizes
@property
def sizes(self):
# dataset.sizes can be a dynamically computed sizes:
return self.dataset.sizes
def get_batch_shapes(self):
if hasattr(self.dataset, "get_batch_shapes"):
return self.dataset.get_batch_shapes()
return self.dataset.buckets
def num_tokens_vec(self, indices):
return self.dataset.num_tokens_vec(indices)
def __len__(self):
return len(self.dataset)
def num_tokens(self, index):
return self.dataset.num_tokens(index)
def size(self, index):
return self.dataset.size(index)
def ordered_indices(self):
return self.dataset.ordered_indices()
@property
def supports_prefetch(self):
return getattr(self.dataset, "supports_prefetch", False)
def prefetch(self, indices):
return self.dataset.prefetch(indices)
def mask_src_tokens(self, sample):
src_item = sample["source"]
mask = None
if self.mask_type == "random":
mask = torch.rand(len(src_item)).le(self.mask_ratio)
else:
mask = torch.ones(len(src_item))
mask[: int(len(src_item) * (1 - self.mask_ratio))] = 0
mask = mask.eq(1)
if src_item[0] == self.src_bos:
mask[0] = False
if src_item[-1] == self.src_eos:
mask[-1] = False
mask_src_item = src_item.masked_fill(mask, self.noise_id)
smp = {"id": sample["id"], "source": mask_src_item, "target": sample["target"]}
return smp
def __getitem__(self, index):
sample = self.dataset[index]
if self.mask_ratio > 0:
sample = self.mask_src_tokens(sample)
return sample
def collater(self, samples, pad_to_length=None):
return self.dataset.collater(samples, pad_to_length)
class FileAudioDatasetWrapper(FileAudioDataset):
def collater(self, samples):
samples = super().collater(samples)
if len(samples) == 0:
return {}
samples["net_input"]["src_tokens"] = samples["net_input"]["source"]
samples["net_input"]["prev_output_tokens"] = None
del samples["net_input"]["source"]
samples["net_input"]["src_lengths"] = None
samples["net_input"]["alignment"] = None
return samples
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