# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
from paddle import distributed as dist
from paddle.io import BatchSampler
from paddle.io import DistributedBatchSampler
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = [
"SortagradDistributedBatchSampler",
"SortagradBatchSampler",
]
def _batch_shuffle(indices, batch_size, epoch, clipped=False):
"""Put similarly-sized instances into minibatches for better efficiency
and make a batch-wise shuffle.
1. Sort the audio clips by duration.
2. Generate a random number `k`, k in [0, batch_size).
3. Randomly shift `k` instances in order to create different batches
for different epochs. Create minibatches.
4. Shuffle the minibatches.
:param indices: indexes. List of int.
:type indices: list
:param batch_size: Batch size. This size is also used for generate
a random number for batch shuffle.
:type batch_size: int
:param clipped: Whether to clip the heading (small shift) and trailing
(incomplete batch) instances.
:type clipped: bool
:return: Batch shuffled mainifest.
:rtype: list
"""
rng = np.random.RandomState(epoch)
shift_len = rng.randint(0, batch_size - 1)
batch_indices = list(zip(* [iter(indices[shift_len:])] * batch_size))
rng.shuffle(batch_indices)
batch_indices = [item for batch in batch_indices for item in batch]
assert clipped is False
if not clipped:
res_len = len(indices) - shift_len - len(batch_indices)
# when res_len is 0, will return whole list, len(List[-0:]) = len(List[:])
if res_len != 0:
batch_indices.extend(indices[-res_len:])
batch_indices.extend(indices[0:shift_len])
assert len(indices) == len(
batch_indices
), f"_batch_shuffle: {len(indices)} : {len(batch_indices)} : {res_len} - {shift_len}"
return batch_indices
[docs]class SortagradDistributedBatchSampler(DistributedBatchSampler):
def __init__(self,
dataset,
batch_size,
num_replicas=None,
rank=None,
shuffle=False,
drop_last=False,
sortagrad=False,
shuffle_method="batch_shuffle"):
"""Sortagrad Sampler for multi gpus.
Args:
dataset (paddle.io.Dataset):
batch_size (int): batch size for one gpu
num_replicas (int, optional): world size or numbers of gpus. Defaults to None.
rank (int, optional): rank id. Defaults to None.
shuffle (bool, optional): True for do shuffle, or else. Defaults to False.
drop_last (bool, optional): whether drop last batch which is less than batch size. Defaults to False.
sortagrad (bool, optional): True, do sortgrad in first epoch, then shuffle as usual; or else. Defaults to False.
shuffle_method (str, optional): shuffle method, "instance_shuffle" or "batch_shuffle". Defaults to "batch_shuffle".
"""
super().__init__(dataset, batch_size, num_replicas, rank, shuffle,
drop_last)
self._sortagrad = sortagrad
self._shuffle_method = shuffle_method
def __iter__(self):
num_samples = len(self.dataset)
indices = np.arange(num_samples).tolist()
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# sort (by duration) or batch-wise shuffle the manifest
if self.shuffle:
if self.epoch == 0 and self._sortagrad:
logger.info(
f'rank: {dist.get_rank()} dataset sortagrad! epoch {self.epoch}'
)
else:
logger.info(
f'rank: {dist.get_rank()} dataset shuffle! epoch {self.epoch}'
)
if self._shuffle_method == "batch_shuffle":
# using `batch_size * nrank`, or will cause instability loss and nan or inf grad,
# since diff batch examlpe length in batches case instability loss in diff rank,
# e.g. rank0 maxlength 20, rank3 maxlength 1000
indices = _batch_shuffle(
indices,
self.batch_size * self.nranks,
self.epoch,
clipped=False)
elif self._shuffle_method == "instance_shuffle":
np.random.RandomState(self.epoch).shuffle(indices)
else:
raise ValueError("Unknown shuffle method %s." %
self._shuffle_method)
assert len(
indices
) == self.total_size, f"batch shuffle examples error: {len(indices)} : {self.total_size}"
# slice `self.batch_size` examples by rank id
def _get_indices_by_batch_size(indices):
subsampled_indices = []
last_batch_size = self.total_size % (self.batch_size * self.nranks)
assert last_batch_size % self.nranks == 0
last_local_batch_size = last_batch_size // self.nranks
for i in range(self.local_rank * self.batch_size,
len(indices) - last_batch_size,
self.batch_size * self.nranks):
subsampled_indices.extend(indices[i:i + self.batch_size])
indices = indices[len(indices) - last_batch_size:]
subsampled_indices.extend(
indices[self.local_rank * last_local_batch_size:(
self.local_rank + 1) * last_local_batch_size])
return subsampled_indices
if self.nranks > 1:
indices = _get_indices_by_batch_size(indices)
assert len(indices) == self.num_samples
_sample_iter = iter(indices)
batch_indices = []
for idx in _sample_iter:
batch_indices.append(idx)
if len(batch_indices) == self.batch_size:
logger.debug(
f"rank: {dist.get_rank()} batch index: {batch_indices} ")
yield batch_indices
batch_indices = []
if not self.drop_last and len(batch_indices) > 0:
yield batch_indices
def __len__(self):
num_samples = self.num_samples
num_samples += int(not self.drop_last) * (self.batch_size - 1)
return num_samples // self.batch_size
[docs]class SortagradBatchSampler(BatchSampler):
def __init__(self,
dataset,
batch_size,
shuffle=False,
drop_last=False,
sortagrad=False,
shuffle_method="batch_shuffle"):
"""Sortagrad Sampler for one gpu.
Args:
dataset (paddle.io.Dataset):
batch_size (int): batch size for one gpu
shuffle (bool, optional): True for do shuffle, or else. Defaults to False.
drop_last (bool, optional): whether drop last batch which is less than batch size. Defaults to False.
sortagrad (bool, optional): True, do sortgrad in first epoch, then shuffle as usual; or else. Defaults to False.
shuffle_method (str, optional): shuffle method, "instance_shuffle" or "batch_shuffle". Defaults to "batch_shuffle".
"""
self.dataset = dataset
assert isinstance(batch_size, int) and batch_size > 0, \
"batch_size should be a positive integer"
self.batch_size = batch_size
assert isinstance(shuffle, bool), \
"shuffle should be a boolean value"
self.shuffle = shuffle
assert isinstance(drop_last, bool), \
"drop_last should be a boolean number"
self.drop_last = drop_last
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0))
self.total_size = self.num_samples
self._sortagrad = sortagrad
self._shuffle_method = shuffle_method
def __iter__(self):
num_samples = len(self.dataset)
indices = np.arange(num_samples).tolist()
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# sort (by duration) or batch-wise shuffle the manifest
if self.shuffle:
if self.epoch == 0 and self._sortagrad:
logger.info(f'dataset sortagrad! epoch {self.epoch}')
else:
logger.info(f'dataset shuffle! epoch {self.epoch}')
if self._shuffle_method == "batch_shuffle":
indices = _batch_shuffle(
indices, self.batch_size, self.epoch, clipped=False)
elif self._shuffle_method == "instance_shuffle":
np.random.RandomState(self.epoch).shuffle(indices)
else:
raise ValueError("Unknown shuffle method %s." %
self._shuffle_method)
assert len(
indices
) == self.total_size, f"batch shuffle examples error: {len(indices)} : {self.total_size}"
assert len(indices) == self.num_samples
_sample_iter = iter(indices)
batch_indices = []
for idx in _sample_iter:
batch_indices.append(idx)
if len(batch_indices) == self.batch_size:
logger.debug(
f"rank: {dist.get_rank()} batch index: {batch_indices} ")
yield batch_indices
batch_indices = []
if not self.drop_last and len(batch_indices) > 0:
yield batch_indices
self.epoch += 1
def __len__(self):
num_samples = self.num_samples
num_samples += int(not self.drop_last) * (self.batch_size - 1)
return num_samples // self.batch_size