@@ -130,8 +130,8 @@ class TorchSingleDriver(TorchDriver): | |||
else: | |||
return self._test_step(batch) | |||
def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleIterator], | |||
reproducible: bool = False, sampler_or_batch_sampler=None): | |||
def set_dist_repro_dataloader(self, dataloader, dist: Union[str, ReproducibleBatchSampler, ReproducibleIterator]=None, | |||
reproducible: bool = False): | |||
if isinstance(dist, ReproducibleBatchSampler): | |||
return replace_batch_sampler(dataloader, dist) | |||
elif isinstance(dist, ReproducibleIterator): | |||
@@ -11,11 +11,11 @@ __all__ = [ | |||
'PollingSampler', | |||
'ReproducibleIterator', | |||
'RandomSampler', | |||
'ReproducibleBatchSampler', | |||
're_instantiate_sampler' | |||
] | |||
from .sampler import BucketSampler, SortedSampler, ConstTokenNumSampler, ConstantTokenNumSampler, UnrepeatedDistributedSampler | |||
from .mix_sampler import MixSampler, InnerSampler, DopedSampler, MixSequentialSampler, PollingSampler | |||
from .reproducible_sampler import ReproducibleIterator, RandomSampler, ReproducibleBatchSampler, re_instantiate_sampler | |||
from .reproducible_sampler import ReproducibleIterator, RandomSampler, re_instantiate_sampler | |||
from .reproducible_batch_sampler import ReproducibleBatchSampler, BucketedBatchSampler | |||
@@ -0,0 +1,397 @@ | |||
__all__ = [ | |||
'BucketedBatchSampler', | |||
"ReproducibleBatchSampler" | |||
] | |||
import math | |||
from array import array | |||
from copy import deepcopy | |||
from typing import Dict, Union, List | |||
from itertools import chain | |||
import numpy as np | |||
from fastNLP.core.dataset import DataSet | |||
from fastNLP.core.log import logger | |||
from abc import abstractmethod | |||
class ReproducibleBatchIterator: | |||
@abstractmethod | |||
def set_distributed(self, num_replicas, rank, pad=True): | |||
raise NotImplementedError("Each specific batch_sampler should implement its own `set_distributed` method.") | |||
@abstractmethod | |||
def __len__(self): | |||
raise NotImplementedError("Each specific batch_sampler should implement its own `__len__` method.") | |||
@abstractmethod | |||
def __iter__(self): | |||
raise NotImplementedError("Each specific batch_sampler should implement its own `__iter__` method.") | |||
@abstractmethod | |||
def state_dict(self): | |||
raise NotImplementedError("Each specific batch_sampler should implement its own `state_dict` method.") | |||
@abstractmethod | |||
def load_state_dict(self, states): | |||
raise NotImplementedError("Each specific batch_sampler should implement its own `load_state_dict` method.") | |||
@abstractmethod | |||
def set_epoch(self, epoch): | |||
pass | |||
class ReproducibleBatchSampler(ReproducibleBatchIterator): | |||
# 这两个参数的值应当交给 driver 的 get_dataloader_args 函数去拿; | |||
def __init__(self, batch_sampler, batch_size: int, drop_last: bool, **kwargs): | |||
""" | |||
可以使得 batch_sampler 对象状态恢复的 wrapper 。 | |||
:param batch_sampler: 可迭代出 数字 或 数字列表 的可迭代对象。ReproducibleBatchSampler 将首先遍历一边该对象,然后将迭代 | |||
出来的序号暂存起来,使用时按照 batch_size 的 batch 大小吐出序号列表。 | |||
:param batch_size: 每个 batch 的大小是多少。 | |||
:param drop_last: 如果最后一个 batch 无法构成 batch_size 那么多个 sample ,是否丢掉。 | |||
:param kwargs: fastNLP 内部使用。 | |||
""" | |||
self.batch_sampler = batch_sampler | |||
self.batch_size = batch_size | |||
self.drop_last = drop_last | |||
self.data_idx = kwargs.get("data_idx", 0) | |||
self.index_list = kwargs.get("index_list", self._iterate_sampler()) | |||
self.need_reinitialize = kwargs.get("need_reinitialize", False) | |||
def _iterate_sampler(self): | |||
_index_lst = [] | |||
for idx in self.batch_sampler: | |||
if isinstance(idx, list): | |||
_index_lst.extend(idx) | |||
# 说明是在初始化时传入的是一个 sampler,理论上对应于 dataloader 在初始化时没有 batch_size,也没有 batch_sampler 的情况; | |||
else: | |||
_index_lst.append(idx) | |||
# 64 位机器的 unsigned int 为 4 个字节,能表示的最大大小为 4294967295; | |||
if len(_index_lst) > 4294967295: | |||
# 注意 self.index_list 内存放的是全部数据的 index; | |||
# unsigned long | |||
_index_lst = array("L", _index_lst) | |||
else: | |||
# unsigned int | |||
_index_lst = array("I", _index_lst) | |||
return _index_lst | |||
def __iter__(self): | |||
if self.need_reinitialize: | |||
self.index_list = self._iterate_sampler() | |||
self.data_idx = 0 | |||
else: | |||
self.need_reinitialize = True | |||
batch = [] | |||
if self.data_idx: | |||
index_list = self.index_list[self.data_idx:] | |||
else: | |||
index_list = self.index_list | |||
for idx in index_list: | |||
batch.append(idx) | |||
self.data_idx += 1 | |||
if len(batch) == self.batch_size: | |||
yield batch | |||
batch = [] | |||
if len(batch) > 0 and not self.drop_last: | |||
yield batch | |||
def __len__(self) -> int: | |||
if self.drop_last: | |||
return len(self.index_list) // self.batch_size | |||
else: | |||
return (len(self.index_list) + self.batch_size - 1) // self.batch_size | |||
def state_dict(self) -> Dict: | |||
return {"index_list": deepcopy(self.index_list), "data_idx": self.data_idx, 'sampler_type': self.__class__.__name__} | |||
def load_state_dict(self, states: Dict): | |||
assert states['sampler_type'] == self.__class__.__name__, f"The sampler type in checkpoint is {states['sampler_type']}," \ | |||
f"we cannot use {self.__class__.__name__} to load it." | |||
_index_list = states["index_list"] | |||
assert len(_index_list) == len(self.index_list), "The number of samples is different between the checkpoint " \ | |||
"record and current dataset." | |||
self.index_list = _index_list | |||
self.data_idx = states["data_idx"] | |||
self.need_reinitialize = False | |||
def set_distributed(self, num_replicas, rank, pad=True): | |||
raise RuntimeError(f"ReproduceBatchSampler does not support to change to distributed training.") | |||
def set_epoch(self, epoch): | |||
if hasattr(self.batch_sampler, "sampler") and hasattr(self.batch_sampler.sampler, 'set_epoch') and callable(self.batch_sampler.sampler.set_epoch): | |||
self.batch_sampler.sampler.set_epoch(epoch) | |||
@property | |||
def batch_idx_in_epoch(self): | |||
if self.drop_last: | |||
return len(self.index_list) // self.batch_size - (len(self.index_list) - self.data_idx) // self.batch_size | |||
else: | |||
return (len(self.index_list) + self.batch_size - 1) // self.batch_size - \ | |||
(len(self.index_list) - self.data_idx + self.batch_size - 1) // self.batch_size | |||
class BucketedBatchSampler(ReproducibleBatchIterator): | |||
def __init__(self, dataset, length: Union[List[int], str], batch_size:int = 32, num_batch_per_bucket:int = 10, | |||
shuffle: bool = True, drop_last: bool = False, seed: int = 0, **kwargs): | |||
""" | |||
首先按照 sample 的长度排序,然后按照 batch_size*num_batch_per_bucket 为一个桶的大小,sample 只会在这个桶内进行组合,这样 | |||
每个 batch 中的 padding 数量会比较少 (因为桶内的数据的长度都接近)。 | |||
:param dataset: 实现了 __len__ 方法的数据容器。 | |||
:param length: 如果为 List,应当与 dataset 有一样的长度,表示 dataset 中每个元素的数量;仅当传入的 dataset 为 fastNLP 的 | |||
DataSet 时支持传入 str,会将该str理解为 dataset 的 field 名称,若 field 中的元素为 int,则认为该值是 sample 的长度。 | |||
如果否则使用 len() 函数得到每个 sample 中这个 field 的长度。 | |||
:param batch_size: 每个 batch 的大小 | |||
:param num_batch_per_bucket: 多少个 batch 组成一个桶,数据只会在一个桶内进行 shuffle 。 | |||
:param shuffle: 如果为 True,将不进行 shuffle,实际上数据会以从长到短的方式输出。 | |||
:param drop_last: 如果最后一个 batch 的 sample 数量无法凑齐 batch_size 这么多,是否需要丢掉。 | |||
:param seed: 设置的随机数种子 | |||
:param kwargs: fastNLP 保留使用 | |||
""" | |||
super().__init__() | |||
if isinstance(dataset, DataSet): | |||
length = dataset.get_field(length) | |||
if not isinstance(length[0], int): | |||
length = list(map(len, length)) | |||
else: | |||
assert len(length) == len(dataset), "When the dataset is not fastNLP.DataSet, " \ | |||
"the length parameter can only be List[int]" | |||
assert len(length) == len(dataset), "The length of `data` and `length` should be equal." | |||
self.dataset = dataset | |||
self.length = np.array(length, dtype=int) # 按照长到短排列的序号。 | |||
self.sorted_indices = np.argsort(self.length)[::-1] # 按长度从高到低排序的 | |||
self.batch_size = batch_size | |||
self.num_batch_per_bucket = num_batch_per_bucket | |||
self.shuffle = shuffle | |||
self.drop_last = drop_last | |||
self.seed = seed | |||
self.num_consumed_samples = kwargs.get("num_consumed_samples", 0) # 总共迭代了多少数据了,包括多卡情况下的其它卡上的输出的数量 | |||
# 多卡的相关的参数 | |||
self.num_replicas = kwargs.get("num_replicas", 1) | |||
self.rank = kwargs.get("rank", 0) | |||
self.epoch = kwargs.get("epoch", -1) | |||
self.pad = kwargs.get("pad", False) # 该参数在单卡上不具有任何意义; | |||
# 是否处于iteration之间,为True不允许调用 set_distributed()和load_state_dict() | |||
self.during_iter = kwargs.get("during_iter", False) | |||
# 以下变量为内部使用恢复状态的变量。 | |||
self.old_batch_size = kwargs.get('old_batch_size', self.batch_size) | |||
self.old_num_batch_per_bucket = kwargs.get('old_num_batch_per_bucket', self.num_batch_per_bucket) | |||
def set_distributed(self, num_replicas, rank, pad=True): | |||
assert self.during_iter is False, "Cannot set the sampler to be distributed when it is " \ | |||
"during an unfinished iteration." | |||
assert num_replicas > 0 and isinstance(num_replicas, int) | |||
assert isinstance(rank, int) and 0 <= rank < num_replicas | |||
# 注意初始化该函数时,所有的状态都应当默认是一个 epoch 刚开始训练的状态; | |||
self.num_replicas = num_replicas | |||
self.rank = rank | |||
self.pad = pad | |||
num_samples = (len(self.dataset)+self.num_replicas-1)//self.num_replicas*self.num_replicas if pad \ | |||
else len(self.dataset) | |||
if self.drop_last: | |||
assert self.num_replicas*self.batch_size<=num_samples, "The number of samples should be greater " \ | |||
"than the number of replicates multiplied " \ | |||
"with batch_size when drop_last=True." | |||
return self | |||
@property | |||
def total_size(self): | |||
""" | |||
这个变量代表的含义是当前这个sampler会最终产生出的index数量(包括了其它rank的),因为replica和pad的原因,这个值可能等于、 | |||
大于或者小于len(dataset) | |||
:return: | |||
""" | |||
return self.num_consumed_samples + self.num_replicas*self.num_left_samples | |||
@property | |||
def num_left_samples(self): | |||
""" | |||
返回当前 iteration 还有多少个 sample 结束,表示的是当前 rank 的还剩多少。 | |||
:return: | |||
""" | |||
num_consumed_samples = self.num_consumed_samples | |||
return math.ceil((len(self.dataset) - num_consumed_samples) / self.num_replicas) if \ | |||
self.pad else math.floor(((len(self.dataset) - num_consumed_samples) / self.num_replicas)) | |||
def __len__(self): | |||
""" | |||
返回当前 sampler 还会返回多少个 batch 的数据 | |||
:return: | |||
""" | |||
num_sampler_per_rank = self.total_size//self.num_replicas | |||
num_batches = num_sampler_per_rank//self.batch_size if self.drop_last else \ | |||
(num_sampler_per_rank+self.batch_size-1)//self.batch_size | |||
return num_batches | |||
def __iter__(self): | |||
if self.during_iter: # 如果发现_during_iter为True,说明之前的还没结束,只有强制重新初始化了 | |||
self.num_consumed_samples = 0 | |||
self.during_iter = True | |||
sorted_indices = deepcopy(self.sorted_indices).tolist() # 按长度从高到低排序的 | |||
if self.shuffle: | |||
if self.num_consumed_samples > 0: # 需要先按照原来的排序,删掉多余的 | |||
_batches = [] | |||
for _i in range(self.old_num_replicas): | |||
_sorted_indices = sorted_indices[_i:len(sorted_indices):self.old_num_replicas] | |||
__batches = self.bucketerize(_sorted_indices, self.old_batch_size, self.old_num_batch_per_bucket, | |||
seed=self.seed+self.epoch) | |||
_batches.append(__batches) | |||
batches = list(chain(*[_ for _ in zip(*_batches)])) | |||
sorted_indices = list(chain(*batches)) | |||
sorted_indices = sorted_indices[self.num_consumed_samples:] | |||
# 再进行排序 | |||
sub_length = self.length[sorted_indices] | |||
sorted_indices = np.array(sorted_indices)[np.argsort(sub_length)[::-1]] # 按长度从高到低排序的 | |||
# 取出这个 rank , | |||
sorted_indices = sorted_indices[self.rank:len(sorted_indices):self.num_replicas] | |||
batches = self.bucketerize(sorted_indices, self.batch_size, self.num_batch_per_bucket, | |||
seed=self.seed+self.epoch) | |||
batches = list(map(list, batches)) | |||
else: | |||
sorted_indices = sorted_indices[self.num_consumed_samples:] | |||
sorted_indices = sorted_indices[self.rank:len(sorted_indices):self.num_replicas] | |||
_num_batches = len(sorted_indices) // self.batch_size | |||
if _num_batches == 0: | |||
batches = [sorted_indices] | |||
else: | |||
batches = list(map(list, np.array_split(sorted_indices[:_num_batches*self.batch_size], _num_batches))) | |||
if len(sorted_indices)%self.batch_size!=0: | |||
batches.append(sorted_indices[_num_batches*self.batch_size:]) | |||
need_pad_num = (len(self.dataset)-self.num_consumed_samples) % self.num_replicas | |||
if self.pad and need_pad_num !=0 and need_pad_num<=self.rank: | |||
if len(batches) > 0: | |||
if len(batches[-1])<self.batch_size: | |||
batches[-1].append(batches[-1][0]) # 这里可以保证这个bucket的长度没被破坏。 | |||
else: | |||
batches.append([batches[-1][0]]) | |||
elif self.pad is False and need_pad_num !=0 and need_pad_num>self.rank: | |||
if len(batches): | |||
batches[-1].pop(-1) | |||
if len(batches[-1])==0: | |||
batches.pop(-1) | |||
assert len(list(chain(*batches))) == self.num_left_samples | |||
if self.drop_last and len(batches) >= 1 and len(batches[-1]) < self.batch_size: | |||
batches = batches[:-1] | |||
for batch in batches: | |||
self.num_consumed_samples += self.num_replicas * len(batch) | |||
yield list(map(int, batch)) | |||
self.during_iter = False | |||
self.num_consumed_samples = 0 | |||
self.old_batch_size = self.batch_size | |||
self.old_num_batch_per_bucket = self.num_batch_per_bucket | |||
self.old_num_replicas = self.num_replicas | |||
if self.epoch < 0: # 防止用户没有修改epoch,导致每个epoch都一样了 | |||
self.epoch -= 1 | |||
def bucketerize(self, sorted_indices, batch_size, num_batch_per_bucket, seed): | |||
""" | |||
将 indices 分桶 | |||
:param sorted_indices: List[int] | |||
:param batch_size: int | |||
:param num_batch_per_bucket: int | |||
:param seed: int | |||
:return: List[List[int]] | |||
""" | |||
# 实际的 bucket 大小 | |||
bucket_size = min(len(sorted_indices), batch_size * num_batch_per_bucket) | |||
rng = np.random.default_rng(abs(seed)) | |||
num_buckets = (len(sorted_indices) + bucket_size - 1) // bucket_size | |||
batches = [] | |||
batch_indices = [] | |||
for i in range(num_buckets): | |||
bucket = sorted_indices[i * bucket_size:(i + 1) * bucket_size] | |||
rng.shuffle(bucket) # bucket 内部 shuffle 一下 | |||
_num_batches = len(bucket) // batch_size | |||
if _num_batches == 0: | |||
_batches = [bucket] | |||
else: | |||
_batches = np.array_split(bucket[:_num_batches*batch_size], _num_batches) | |||
if len(bucket) % batch_size != 0: | |||
_batches.append(bucket[_num_batches*batch_size:]) | |||
batch_indices.extend(list(range(len(batches), len(batches) + len(_batches)))) | |||
batches.extend(_batches) | |||
last_batches = [] | |||
# 最后一个batch 统一不参与shuffle,因为有的rank最后一个 batch 可能不足一个batch_size (不足的时候 | |||
# 一定要放在末尾,所以就干脆所有的rank都不对最后一个batch进行shuffle)。 | |||
if len(batches) >= 1: | |||
last_batches = [list(batches[-1])] | |||
batch_indices = list(batch_indices[:-1]) | |||
rng = np.random.default_rng(abs(seed)) # 这里防止由于bucket长度不同,对随机数状态有影响 | |||
rng.shuffle(batch_indices) # 不同的 batch 也 shuffle ,当前这种可以保证每张卡上每个 batch 长度都接近的。 | |||
batches = (np.array(batches)[batch_indices]).tolist() | |||
if last_batches: | |||
batches = batches + last_batches | |||
return batches | |||
def state_dict(self) -> Dict: | |||
if self.old_batch_size != self.batch_size or self.old_num_batch_per_bucket != self.num_batch_per_bucket: | |||
raise RuntimeError("BucketedBatchSampler does not support saving before last checkpoint states have been" | |||
" consumed. ") | |||
states = { | |||
'seed': self.seed, | |||
'epoch': self.epoch, | |||
'num_consumed_samples': self.num_consumed_samples, # 注意该值是计算所有 rank 上训练的所有数据; | |||
'sampler_type': self.__class__.__name__, | |||
'length': len(self.dataset), | |||
'shuffle': self.shuffle, | |||
'batch_size': self.batch_size, | |||
'num_batch_per_bucket': self.num_batch_per_bucket, | |||
'num_replicas': self.num_replicas | |||
} | |||
return states | |||
def load_state_dict(self, states: Dict): | |||
# 如果 self.during_iter 是 True,那么 data_idx 一定是 0; | |||
assert self.during_iter is False, "Cannot call load_state_dict() when it is " \ | |||
"during an unfinished iteration." | |||
assert states['sampler_type'] == self.__class__.__name__, f"The sampler type in checkpoint is {states['sampler_type']}," \ | |||
f"we cannot use {self.__class__.__name__} to load it." | |||
length = states['length'] | |||
assert length == len(self.dataset), "The number of samples is different between the checkpoint record " \ | |||
"and current dataset." | |||
self.seed = states['seed'] | |||
self.epoch = states['epoch'] | |||
self.num_consumed_samples = states['num_consumed_samples'] | |||
if self.num_consumed_samples>=length: # 如果保存的时候已经到达了最后一个sample了,则直接将结果重置为0 | |||
self.num_consumed_samples = 0 | |||
if self.shuffle != states['shuffle']: | |||
logger.info(f"The shuffle from the checkpoint is {states['shuffle']}, while set as {self.shuffle}, " | |||
f"we use shuffle={states['shuffle']}") | |||
self.shuffle = states["shuffle"] | |||
self.old_batch_size = states['batch_size'] | |||
self.old_num_batch_per_bucket = states['num_batch_per_bucket'] | |||
self.old_num_replicas = states['num_replicas'] | |||
def set_epoch(self, epoch): | |||
self.epoch = epoch |
@@ -1,14 +1,12 @@ | |||
from typing import Dict, List | |||
import math | |||
import numpy as np | |||
from array import array | |||
from copy import deepcopy | |||
from fastNLP.core.log import logger | |||
__all__ = [ | |||
'ReproducibleIterator', | |||
'RandomSampler', | |||
'ReproducibleBatchSampler', | |||
're_instantiate_sampler' | |||
] | |||
@@ -22,7 +20,8 @@ def re_instantiate_sampler(sampler): | |||
class ReproducibleIterator: | |||
""" | |||
注意所有继承 `ReproducibleIterator` 的类的 `__init__` 方法中都需要加入参数 `**kwargs`,用来使我们再断点重训时重新实例化这个 sampler | |||
或者 batch_sampler; | |||
或者 batch_sampler;注意,所有在 init 中初始化的变量,都不能含有 _ 下横线作为开头;所有不在 init 中设置的变量都必须以下横线开头。 | |||
""" | |||
def set_distributed(self, num_replicas, rank, pad=True): | |||
@@ -72,7 +71,7 @@ class RandomSampler(ReproducibleIterator): | |||
self.pad = kwargs.get("pad", False) # 该参数在单卡上不具有任何意义; | |||
# 是否处于iteration之间,为True不允许调用 set_distributed()和load_state_dict() | |||
self._during_iter = kwargs.get("_during_iter", False) | |||
self.during_iter = kwargs.get("during_iter", False) | |||
def __len__(self): | |||
""" | |||
@@ -92,9 +91,9 @@ class RandomSampler(ReproducibleIterator): | |||
>>> next(iter2) # 当前num_consumed_samples的数量会发生变化 | |||
""" | |||
if self._during_iter: # 如果发现_during_iter为True,说明之前的还没结束,只有强制重新初始化了 | |||
if self.during_iter: # 如果发现_during_iter为True,说明之前的还没结束,只有强制重新初始化了 | |||
self.num_consumed_samples = 0 | |||
self._during_iter = True | |||
self.during_iter = True | |||
indices = self.generate_indices() | |||
if self.pad: | |||
@@ -118,7 +117,7 @@ class RandomSampler(ReproducibleIterator): | |||
for index in indices: | |||
self.num_consumed_samples += self.num_replicas | |||
yield index | |||
self._during_iter = False | |||
self.during_iter = False | |||
self.num_consumed_samples = 0 | |||
def generate_indices(self) -> List[int]: | |||
@@ -150,8 +149,8 @@ class RandomSampler(ReproducibleIterator): | |||
return states | |||
def load_state_dict(self, states: Dict): | |||
# 如果 self._during_iter 是 True,那么 data_idx 一定是 0; | |||
assert self._during_iter is False, "Cannot call load_state_dict() when it is " \ | |||
# 如果 self.during_iter 是 True,那么 data_idx 一定是 0; | |||
assert self.during_iter is False, "Cannot call load_state_dict() when it is " \ | |||
"during an unfinished iteration." | |||
assert states['sampler_type'] == self.__class__.__name__, f"The sampler type in checkpoint is {states['sampler_type']}," \ | |||
@@ -165,6 +164,9 @@ class RandomSampler(ReproducibleIterator): | |||
self.num_consumed_samples = states['num_consumed_samples'] | |||
if self.num_consumed_samples>=length: # 如果保存的时候已经到达了最后一个sample了,则直接将结果重置为0 | |||
self.num_consumed_samples = 0 | |||
if self.shuffle != states['shuffle']: | |||
logger.info(f"The shuffle from the checkpoint is {states['shuffle']}, while set as {self.shuffle}, " | |||
f"we use shuffle={states['shuffle']}") | |||
self.shuffle = states["shuffle"] | |||
def set_epoch(self, epoch: int) -> None: | |||
@@ -181,7 +183,7 @@ class RandomSampler(ReproducibleIterator): | |||
:return: | |||
""" | |||
assert self._during_iter is False, "Cannot set the sampler to be distributed when it is " \ | |||
assert self.during_iter is False, "Cannot set the sampler to be distributed when it is " \ | |||
"during an unfinished iteration." | |||
assert num_replicas>0 and isinstance(num_replicas, int) | |||
assert isinstance(rank, int) and 0<=rank<num_replicas | |||
@@ -204,7 +206,7 @@ class RandomSampler(ReproducibleIterator): | |||
@property | |||
def num_left_samples(self): | |||
""" | |||
返回当前 iteration 还有多少个 sample 结束 | |||
返回当前 iteration 还有多少个 sample 结束。表示的是当前 rank 的还剩多少 | |||
:return: | |||
""" | |||
@@ -213,119 +215,8 @@ class RandomSampler(ReproducibleIterator): | |||
self.pad else math.floor(((len(self.dataset) - num_consumed_samples) / self.num_replicas)) | |||
class ReproducibleBatchSampler: | |||
# 这两个参数的值应当交给 driver 的 get_dataloader_args 函数去拿; | |||
def __init__(self, batch_sampler, batch_size: int, drop_last: bool, **kwargs): | |||
""" | |||
可以使得 batch_sampler 对象状态恢复的 wrapper 。 | |||
:param batch_sampler: 可迭代出 数字 或 数字列表 的可迭代对象。ReproducibleBatchSampler 将首先遍历一边该对象,然后将迭代 | |||
出来的序号暂存起来,使用时按照 batch_size 的 batch 大小吐出序号列表。 | |||
:param batch_size: 每个 batch 的大小是多少。 | |||
:param drop_last: 如果最后一个 batch 无法构成 batch_size 那么多个 sample ,是否丢掉。 | |||
:param kwargs: fastNLP 内部使用。 | |||
""" | |||
self.batch_sampler = batch_sampler | |||
self.batch_size = batch_size | |||
self.drop_last = drop_last | |||
self.data_idx = kwargs.get("data_idx", 0) | |||
self._index_list = kwargs.get("_index_list", self._iterate_sampler()) | |||
self.need_reinitialize = kwargs.get("need_reinitialize", False) | |||
def _iterate_sampler(self): | |||
_index_lst = [] | |||
for idx in self.batch_sampler: | |||
if isinstance(idx, list): | |||
_index_lst.extend(idx) | |||
# 说明是在初始化时传入的是一个 sampler,理论上对应于 dataloader 在初始化时没有 batch_size,也没有 batch_sampler 的情况; | |||
else: | |||
_index_lst.append(idx) | |||
# 64 位机器的 unsigned int 为 4 个字节,能表示的最大大小为 4294967295; | |||
if len(_index_lst) > 4294967295: | |||
# 注意 self._index_list 内存放的是全部数据的 index; | |||
# unsigned long | |||
_index_lst = array("L", _index_lst) | |||
else: | |||
# unsigned int | |||
_index_lst = array("I", _index_lst) | |||
return _index_lst | |||
def __iter__(self): | |||
if self.need_reinitialize: | |||
self._index_list = self._iterate_sampler() | |||
self.data_idx = 0 | |||
else: | |||
self.need_reinitialize = True | |||
batch = [] | |||
if self.data_idx: | |||
index_list = self._index_list[self.data_idx:] | |||
else: | |||
index_list = self._index_list | |||
for idx in index_list: | |||
batch.append(idx) | |||
self.data_idx += 1 | |||
if len(batch) == self.batch_size: | |||
yield batch | |||
batch = [] | |||
if len(batch) > 0 and not self.drop_last: | |||
yield batch | |||
def __len__(self) -> int: | |||
if self.drop_last: | |||
return len(self._index_list) // self.batch_size | |||
else: | |||
return (len(self._index_list) + self.batch_size - 1) // self.batch_size | |||
def state_dict(self) -> Dict: | |||
return {"index_list": deepcopy(self._index_list), "data_idx": self.data_idx, 'sampler_type': self.__class__.__name__} | |||
def load_state_dict(self, states: Dict): | |||
assert states['sampler_type'] == self.__class__.__name__, f"The sampler type in checkpoint is {states['sampler_type']}," \ | |||
f"we cannot use {self.__class__.__name__} to load it." | |||
_index_list = states["index_list"] | |||
assert len(_index_list) == len(self._index_list), "The number of samples is different between the checkpoint " \ | |||
"record and current dataset." | |||
self._index_list = _index_list | |||
self.data_idx = states["data_idx"] | |||
self.need_reinitialize = False | |||
def set_distributed(self): | |||
raise RuntimeError(f"ReproduceBatchSampler does not support to change to distributed training.") | |||
def set_epoch(self, epoch): | |||
if hasattr(self.batch_sampler, "sampler") and hasattr(self.batch_sampler.sampler, 'set_epoch') and callable(self.batch_sampler.sampler.set_epoch): | |||
self.batch_sampler.sampler.set_epoch(epoch) | |||
@property | |||
def batch_idx_in_epoch(self): | |||
if self.drop_last: | |||
return len(self._index_list) // self.batch_size - (len(self._index_list) - self.data_idx) // self.batch_size | |||
else: | |||
return (len(self._index_list) + self.batch_size - 1) // self.batch_size - \ | |||
(len(self._index_list) - self.data_idx + self.batch_size - 1) // self.batch_size | |||
# todo | |||
# class SortedSampler(ReproducibleIterator): | |||
# def __init__(self, dataset, key): | |||
# pass | |||
# | |||
# | |||
# class BucketedSampler(ReproducibleIterator): | |||
# def __init__(self, dataset, key): | |||
# pass | |||
if __name__ == "__main__": | |||
sampler = RandomSampler(1) | |||
print(vars(sampler)) | |||
batch_sampler = ReproducibleBatchSampler(list(range(3)), 1, True) | |||
print(vars(batch_sampler)) | |||
@@ -9,7 +9,8 @@ import paddle | |||
from paddle.io import DataLoader, BatchSampler | |||
from fastNLP.core.drivers.paddle_driver.single_device import PaddleSingleDriver | |||
from fastNLP.core.samplers.reproducible_sampler import ReproducibleBatchSampler, RandomSampler | |||
from fastNLP.core.samplers.reproducible_sampler import RandomSampler | |||
from fastNLP.core.samplers import ReproducibleBatchSampler | |||
from tests.helpers.models.paddle_model import PaddleNormalModel_Classification | |||
from tests.helpers.datasets.paddle_data import PaddleDataset_MNIST, PaddleRandomDataset | |||
from fastNLP.core import synchronize_safe_rm | |||
@@ -0,0 +1,439 @@ | |||
from array import array | |||
import numpy as np | |||
import pytest | |||
from itertools import chain | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, BucketedBatchSampler | |||
from fastNLP.core.drivers.torch_driver.utils import replace_batch_sampler | |||
from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
class TestReproducibleBatchSampler: | |||
# TODO 拆分测试,在这里只测试一个东西 | |||
def test_torch_dataloader_1(self): | |||
import torch | |||
from torch.utils.data import DataLoader | |||
# no shuffle | |||
before_batch_size = 7 | |||
dataset = TorchNormalDataset(num_of_data=100) | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
forward_steps = 3 | |||
iter_dataloader = iter(dataloader) | |||
for _ in range(forward_steps): | |||
next(iter_dataloader) | |||
# 1. 保存状态 | |||
_get_re_batchsampler = dataloader.batch_sampler | |||
assert isinstance(_get_re_batchsampler, ReproducibleBatchSampler) | |||
state = _get_re_batchsampler.state_dict() | |||
assert state == {"index_list": array("I", list(range(100))), "data_idx": forward_steps*before_batch_size, | |||
"sampler_type": "ReproducibleBatchSampler"} | |||
# 2. 断点重训,重新生成一个 dataloader; | |||
# 不改变 batch_size; | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
re_batchsampler.load_state_dict(state) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
real_res = [] | |||
supposed_res = (torch.tensor(list(range(21, 28))), torch.tensor(list(range(28, 35)))) | |||
forward_steps = 2 | |||
iter_dataloader = iter(dataloader) | |||
for _ in range(forward_steps): | |||
real_res.append(next(iter_dataloader)) | |||
for i in range(forward_steps): | |||
assert all(real_res[i] == supposed_res[i]) | |||
# 改变 batch_size; | |||
after_batch_size = 3 | |||
dataloader = DataLoader(dataset, batch_size=after_batch_size) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
re_batchsampler.load_state_dict(state) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
real_res = [] | |||
supposed_res = (torch.tensor(list(range(21, 24))), torch.tensor(list(range(24, 27)))) | |||
forward_steps = 2 | |||
iter_dataloader = iter(dataloader) | |||
for _ in range(forward_steps): | |||
real_res.append(next(iter_dataloader)) | |||
for i in range(forward_steps): | |||
assert all(real_res[i] == supposed_res[i]) | |||
# 断点重训的第二轮是否是一个完整的 dataloader; | |||
# 先把断点重训所在的那一个 epoch 跑完; | |||
begin_idx = 27 | |||
while True: | |||
try: | |||
data = next(iter_dataloader) | |||
_batch_size = len(data) | |||
assert all(data == torch.tensor(list(range(begin_idx, begin_idx + _batch_size)))) | |||
begin_idx += _batch_size | |||
except StopIteration: | |||
break | |||
# 开始新的一轮; | |||
begin_idx = 0 | |||
iter_dataloader = iter(dataloader) | |||
while True: | |||
try: | |||
data = next(iter_dataloader) | |||
_batch_size = len(data) | |||
assert all(data == torch.tensor(list(range(begin_idx, begin_idx + _batch_size)))) | |||
begin_idx += _batch_size | |||
except StopIteration: | |||
break | |||
def test_torch_dataloader_2(self): | |||
# 测试新的一轮的 index list 是重新生成的,而不是沿用上一轮的; | |||
from torch.utils.data import DataLoader | |||
# no shuffle | |||
before_batch_size = 7 | |||
dataset = TorchNormalDataset(num_of_data=100) | |||
# 开启 shuffle,来检验断点重训后的第二轮的 index list 是不是重新生成的; | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size, shuffle=True) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
# 将一轮的所有数据保存下来,看是否恢复的是正确的; | |||
all_supposed_data = [] | |||
forward_steps = 3 | |||
iter_dataloader = iter(dataloader) | |||
for _ in range(forward_steps): | |||
all_supposed_data.extend(next(iter_dataloader).tolist()) | |||
# 1. 保存状态 | |||
_get_re_batchsampler = dataloader.batch_sampler | |||
assert isinstance(_get_re_batchsampler, ReproducibleBatchSampler) | |||
state = _get_re_batchsampler.state_dict() | |||
# 2. 断点重训,重新生成一个 dataloader; | |||
# 不改变 batch_size; | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size, shuffle=True) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
re_batchsampler.load_state_dict(state) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
# 先把这一轮的数据过完; | |||
pre_index_list = dataloader.batch_sampler.state_dict()["index_list"] | |||
while True: | |||
try: | |||
all_supposed_data.extend(next(iter_dataloader).tolist()) | |||
except StopIteration: | |||
break | |||
assert all_supposed_data == list(pre_index_list) | |||
# 重新开启新的一轮; | |||
for _ in range(3): | |||
iter_dataloader = iter(dataloader) | |||
res = [] | |||
while True: | |||
try: | |||
res.append(next(iter_dataloader)) | |||
except StopIteration: | |||
break | |||
def test_3(self): | |||
import torch | |||
from torch.utils.data import DataLoader | |||
before_batch_size = 7 | |||
dataset = TorchNormalDataset(num_of_data=100) | |||
# 开启 shuffle,来检验断点重训后的第二轮的 index list 是不是重新生成的; | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size) | |||
for idx, data in enumerate(dataloader): | |||
if idx > 3: | |||
break | |||
iterator = iter(dataloader) | |||
for each in iterator: | |||
pass | |||
class DatasetWithVaryLength: | |||
def __init__(self, num_of_data=100): | |||
self.data = np.arange(num_of_data) | |||
def __getitem__(self, item): | |||
return self.data[item] | |||
def __len__(self): | |||
return len(self.data) | |||
class TestBucketedBatchSampler: | |||
@pytest.mark.parametrize('shuffle', [True, False]) | |||
@pytest.mark.parametrize('drop_last', [True, False]) | |||
@pytest.mark.parametrize('num', [2, 7, 14, 15, 70, 71]) | |||
def test_single_num_batch(self, shuffle, drop_last, num): | |||
# 数量不够不报错 | |||
for num in [2, 7, 14, 15, 70, 71]: | |||
dataset = DatasetWithVaryLength(num_of_data=num) | |||
before_batch_size = 7 | |||
re_batchsampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size, | |||
num_batch_per_bucket=10, drop_last=drop_last, | |||
shuffle=shuffle) | |||
count = len(list(iter(re_batchsampler))) | |||
if drop_last: | |||
assert count==num//before_batch_size, num | |||
else: | |||
assert count==(num+before_batch_size-1)//before_batch_size, num | |||
@pytest.mark.parametrize('shuffle', [True, False]) | |||
@pytest.mark.parametrize('drop_last', [True, False]) | |||
def test_single(self, shuffle, drop_last): | |||
before_batch_size = 7 | |||
num_batch_per_bucket = 4 # 那么任意 batch 内的长度差值不应该超过4 | |||
dataset = DatasetWithVaryLength(num_of_data=1000) | |||
re_batchsampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size, | |||
num_batch_per_bucket=num_batch_per_bucket, drop_last=drop_last, | |||
shuffle=shuffle) | |||
re_batchsampler.set_epoch(0) | |||
forward_steps = 10 | |||
iterator = iter(re_batchsampler) | |||
already_generate_indices = set() | |||
for _ in range(forward_steps): | |||
batch = next(iterator) | |||
assert max(batch) - min(batch) <= before_batch_size * num_batch_per_bucket | |||
already_generate_indices.update(batch) | |||
# 1. 保存状态 | |||
state = re_batchsampler.state_dict() | |||
# 2. 断点重训,继续训练 | |||
re_batchsampler2 = BucketedBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size, | |||
num_batch_per_bucket=num_batch_per_bucket, drop_last=drop_last, | |||
shuffle=shuffle) | |||
re_batchsampler2.load_state_dict(state) | |||
re_batchsampler2.set_epoch(0) | |||
new_already_generate_indices = set() | |||
mask = np.ones(len(dataset), dtype=bool) | |||
mask[list(already_generate_indices)] = 0 | |||
indices = np.arange(len(dataset))[mask] | |||
max_diff = -1 | |||
for i in range(len(indices)-before_batch_size * num_batch_per_bucket): | |||
max_diff = max(max_diff, indices[i+before_batch_size * num_batch_per_bucket]-indices[i]) | |||
for batch in re_batchsampler2: | |||
assert max(batch) - min(batch) <= max_diff | |||
for b in batch: | |||
assert b not in already_generate_indices | |||
new_already_generate_indices.update(batch) | |||
if drop_last is False: | |||
assert len(new_already_generate_indices.union(already_generate_indices))==len(dataset) | |||
# 改变 batch_size; | |||
after_batch_size = 3 | |||
re_batchsampler3 = BucketedBatchSampler(dataset, length=dataset.data, batch_size=after_batch_size, | |||
num_batch_per_bucket=num_batch_per_bucket, drop_last=drop_last, | |||
shuffle=shuffle) | |||
re_batchsampler3.load_state_dict(state) | |||
re_batchsampler3.set_epoch(0) | |||
count = 0 | |||
mask = np.ones(len(dataset), dtype=bool) | |||
mask[list(already_generate_indices)] = 0 | |||
indices = np.arange(len(dataset))[mask] | |||
max_diff = -1 | |||
for i in range(len(indices)-after_batch_size * num_batch_per_bucket): | |||
max_diff = max(max_diff, indices[i+after_batch_size * num_batch_per_bucket]-indices[i]) | |||
for batch in re_batchsampler3: | |||
assert max(batch) - min(batch) <= max_diff | |||
for b in batch: | |||
assert b not in already_generate_indices | |||
already_generate_indices.update(batch) | |||
count += 1 | |||
if count > 5: | |||
break | |||
# 再 save ,不允许再上个epoch没结束继续sample | |||
after_batch_size = 5 | |||
with pytest.raises(RuntimeError): | |||
state = re_batchsampler3.state_dict() | |||
for batch in re_batchsampler3: # consume all, 这样才能save | |||
pass | |||
already_generate_indices = set() | |||
count = 0 | |||
for batch in re_batchsampler3: # 重新开始 | |||
assert max(batch) - min(batch) <= max_diff | |||
for b in batch: | |||
assert b not in already_generate_indices | |||
already_generate_indices.update(batch) | |||
count += 1 | |||
if count > 5: | |||
break | |||
state = re_batchsampler3.state_dict() | |||
# 这里的 drop_last 为 False,需要最终是所有 sample | |||
re_batchsampler4 = BucketedBatchSampler(dataset, length=dataset.data, batch_size=after_batch_size, | |||
num_batch_per_bucket=num_batch_per_bucket, drop_last=False, | |||
shuffle=shuffle) | |||
re_batchsampler4.load_state_dict(state) | |||
re_batchsampler4.set_epoch(0) | |||
mask = np.ones(len(dataset), dtype=bool) | |||
mask[list(already_generate_indices)] = 0 | |||
indices = np.arange(len(dataset))[mask] | |||
max_diff = -1 | |||
for i in range(len(indices) - after_batch_size * num_batch_per_bucket): | |||
max_diff = max(max_diff, indices[i + after_batch_size * num_batch_per_bucket] - indices[i]) | |||
for batch in re_batchsampler4: | |||
assert max(batch) - min(batch) <= max_diff | |||
for b in batch: | |||
assert b not in already_generate_indices | |||
already_generate_indices.update(batch) | |||
assert len(already_generate_indices) == len(dataset) | |||
@pytest.mark.parametrize('shuffle', [True, False]) | |||
@pytest.mark.parametrize('drop_last', [True, False]) | |||
@pytest.mark.parametrize('pad', [True, False]) | |||
def test_multi(self, shuffle, drop_last, pad): | |||
# def test_multi(self, shuffle=True, drop_last=False, pad=False): | |||
# no shuffle | |||
num_replica = 2 | |||
dataset = DatasetWithVaryLength(num_of_data=1000) | |||
batch_size = 5 | |||
num_batch_per_bucket = 10 | |||
lengths = [] | |||
rank0_already_seen_indexes = None | |||
max_diff = num_batch_per_bucket * batch_size * num_replica | |||
for rank in range(num_replica): | |||
sampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size = batch_size, | |||
num_batch_per_bucket = num_batch_per_bucket, | |||
shuffle = shuffle, drop_last=drop_last) | |||
sampler.set_epoch(0) | |||
sampler.set_distributed(num_replica, rank=rank, pad=pad) | |||
lengths.append(len(sampler)) | |||
already_seen_indexes = set() | |||
repeat_count = 0 | |||
for batch in sampler: | |||
assert max_diff>=max(batch)-min(batch) | |||
for b in batch: | |||
repeat_count += int(b in already_seen_indexes) | |||
if rank0_already_seen_indexes: # 不能交叉出现 | |||
assert b not in rank0_already_seen_indexes | |||
already_seen_indexes.update(batch) | |||
if rank0_already_seen_indexes is None: | |||
rank0_already_seen_indexes = already_seen_indexes | |||
if pad: # 应该允许重复一次 | |||
assert repeat_count<=1 | |||
else: | |||
assert repeat_count==0 | |||
assert len(set(lengths))==1, lengths # 每个进程的batch数量一致 | |||
# 多进程的保存 | |||
already_seen_indexes = set() | |||
for rank in range(num_replica): | |||
sampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size = batch_size, | |||
num_batch_per_bucket = num_batch_per_bucket, | |||
shuffle = shuffle, drop_last=drop_last) | |||
sampler.set_epoch(0) | |||
sampler.set_distributed(num_replica, rank=rank, pad=pad) | |||
lengths.append(len(sampler)) | |||
count = 0 | |||
for batch in sampler: | |||
assert max_diff>=max(batch)-min(batch) | |||
already_seen_indexes.update(batch) | |||
if count>5: | |||
break | |||
count += 1 | |||
state = sampler.state_dict() | |||
# 切换成单机 | |||
new_batch_size = 6 | |||
num_batch_per_bucket = 3 | |||
new_sampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size=new_batch_size, | |||
num_batch_per_bucket=num_batch_per_bucket, | |||
shuffle=shuffle, drop_last=drop_last) | |||
new_sampler.load_state_dict(state) | |||
repeat_count = 0 | |||
new_already_seen_indexes = set(list(already_seen_indexes)) | |||
mask = np.ones(len(dataset), dtype=bool) | |||
mask[list(already_seen_indexes)] = 0 | |||
indices = np.arange(len(dataset))[mask] | |||
max_diff = -1 | |||
for i in range(len(indices)-new_batch_size * num_batch_per_bucket): | |||
max_diff = max(max_diff, indices[i+new_batch_size * num_batch_per_bucket]-indices[i]) | |||
for batch in new_sampler: | |||
assert max_diff>=max(batch)-min(batch) | |||
for b in batch: | |||
repeat_count += int(b in new_already_seen_indexes) | |||
new_already_seen_indexes.update(batch) | |||
if pad: # 应该允许重复一次 | |||
assert repeat_count <= 1 | |||
else: | |||
assert repeat_count == 0 | |||
if drop_last is False: # 如果没有drop应该相等 | |||
assert len(new_already_seen_indexes)==len(dataset) | |||
# 测试替换卡的数量。 | |||
num_replica = 3 | |||
new_sampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size=new_batch_size, | |||
num_batch_per_bucket=num_batch_per_bucket, | |||
shuffle=shuffle, drop_last=drop_last) | |||
new_sampler.set_epoch(0) | |||
new_sampler.load_state_dict(state) | |||
new_sampler.set_distributed(num_replicas=num_replica, rank=1, pad=pad) | |||
repeat_count = 0 | |||
mask = np.ones(len(dataset), dtype=bool) | |||
mask[list(already_seen_indexes)] = 0 | |||
indices = np.arange(len(dataset))[mask] | |||
max_diff = -1 | |||
for i in range(len(indices) - new_batch_size * num_batch_per_bucket*num_replica): | |||
max_diff = max(max_diff, indices[i + new_batch_size * num_batch_per_bucket*num_replica] - indices[i]) | |||
for batch in new_sampler: | |||
assert max_diff>=max(batch)-min(batch) | |||
for b in batch: | |||
repeat_count += int(b in already_seen_indexes) | |||
if pad: # 应该允许重复一次 | |||
assert repeat_count <= 1 | |||
else: | |||
assert repeat_count == 0 | |||
@pytest.mark.parametrize('shuffle', [True, False]) | |||
@pytest.mark.parametrize('drop_last', [True, False]) | |||
@pytest.mark.parametrize('pad', [True, False]) | |||
@pytest.mark.parametrize('num_samples', [13, 100, 623, 1000]) | |||
@pytest.mark.parametrize('num_replica', [2, 3]) | |||
def test_multi_same_bucket(self, shuffle, drop_last, pad, num_samples, num_replica): | |||
# def test_multi_same_bucket(self, shuffle=True, drop_last=True, pad=True, num_samples=623, num_replica=2): | |||
# TODO 两个 rank 上的长度是要在同一个bucket的 | |||
dataset = DatasetWithVaryLength(num_of_data=num_samples) | |||
batch_size = 6 | |||
if num_replica*batch_size > num_samples: | |||
return | |||
num_batch_per_bucket = 10 | |||
samplers = [] | |||
lengths = [] | |||
for i in range(num_replica): | |||
sampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size=batch_size, | |||
num_batch_per_bucket=num_batch_per_bucket, shuffle=shuffle, drop_last=drop_last) | |||
sampler.set_distributed(num_replica, rank=i, pad=pad) | |||
sampler.set_epoch(0) | |||
samplers.append(sampler) | |||
lengths.append(len(list(iter(sampler)))) | |||
assert len(set(lengths))==1 | |||
bucket_diff = batch_size * num_batch_per_bucket * num_replica | |||
for bs in zip(*samplers): | |||
diff = max(chain(*bs)) - min(chain(*bs)) | |||
assert diff <= bucket_diff |
@@ -6,7 +6,7 @@ import numpy as np | |||
from functools import partial | |||
from array import array | |||
from fastNLP.core.samplers.reproducible_sampler import RandomSampler, ReproducibleBatchSampler | |||
from fastNLP.core.samplers.reproducible_sampler import RandomSampler | |||
from fastNLP.core.drivers.torch_driver.utils import replace_batch_sampler | |||
from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
@@ -361,148 +361,3 @@ class TestRandomSampler(unittest.TestCase): | |||
class TestReproducibleBatchSampler: | |||
def test_torch_dataloader_1(self): | |||
import torch | |||
from torch.utils.data import DataLoader | |||
# no shuffle | |||
before_batch_size = 7 | |||
dataset = TorchNormalDataset(num_of_data=100) | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
forward_steps = 3 | |||
iter_dataloader = iter(dataloader) | |||
for _ in range(forward_steps): | |||
next(iter_dataloader) | |||
# 1. 保存状态 | |||
_get_re_batchsampler = dataloader.batch_sampler | |||
assert isinstance(_get_re_batchsampler, ReproducibleBatchSampler) | |||
state = _get_re_batchsampler.state_dict() | |||
assert state == {"index_list": array("I", list(range(100))), "data_idx": forward_steps*before_batch_size, | |||
"sampler_type": "ReproducibleBatchSampler"} | |||
# 2. 断点重训,重新生成一个 dataloader; | |||
# 不改变 batch_size; | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
re_batchsampler.load_state_dict(state) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
real_res = [] | |||
supposed_res = (torch.tensor(list(range(21, 28))), torch.tensor(list(range(28, 35)))) | |||
forward_steps = 2 | |||
iter_dataloader = iter(dataloader) | |||
for _ in range(forward_steps): | |||
real_res.append(next(iter_dataloader)) | |||
for i in range(forward_steps): | |||
assert all(real_res[i] == supposed_res[i]) | |||
# 改变 batch_size; | |||
after_batch_size = 3 | |||
dataloader = DataLoader(dataset, batch_size=after_batch_size) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
re_batchsampler.load_state_dict(state) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
real_res = [] | |||
supposed_res = (torch.tensor(list(range(21, 24))), torch.tensor(list(range(24, 27)))) | |||
forward_steps = 2 | |||
iter_dataloader = iter(dataloader) | |||
for _ in range(forward_steps): | |||
real_res.append(next(iter_dataloader)) | |||
for i in range(forward_steps): | |||
assert all(real_res[i] == supposed_res[i]) | |||
# 断点重训的第二轮是否是一个完整的 dataloader; | |||
# 先把断点重训所在的那一个 epoch 跑完; | |||
begin_idx = 27 | |||
while True: | |||
try: | |||
data = next(iter_dataloader) | |||
_batch_size = len(data) | |||
assert all(data == torch.tensor(list(range(begin_idx, begin_idx + _batch_size)))) | |||
begin_idx += _batch_size | |||
except StopIteration: | |||
break | |||
# 开始新的一轮; | |||
begin_idx = 0 | |||
iter_dataloader = iter(dataloader) | |||
while True: | |||
try: | |||
data = next(iter_dataloader) | |||
_batch_size = len(data) | |||
assert all(data == torch.tensor(list(range(begin_idx, begin_idx + _batch_size)))) | |||
begin_idx += _batch_size | |||
except StopIteration: | |||
break | |||
def test_torch_dataloader_2(self): | |||
# 测试新的一轮的 index list 是重新生成的,而不是沿用上一轮的; | |||
from torch.utils.data import DataLoader | |||
# no shuffle | |||
before_batch_size = 7 | |||
dataset = TorchNormalDataset(num_of_data=100) | |||
# 开启 shuffle,来检验断点重训后的第二轮的 index list 是不是重新生成的; | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size, shuffle=True) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
# 将一轮的所有数据保存下来,看是否恢复的是正确的; | |||
all_supposed_data = [] | |||
forward_steps = 3 | |||
iter_dataloader = iter(dataloader) | |||
for _ in range(forward_steps): | |||
all_supposed_data.extend(next(iter_dataloader).tolist()) | |||
# 1. 保存状态 | |||
_get_re_batchsampler = dataloader.batch_sampler | |||
assert isinstance(_get_re_batchsampler, ReproducibleBatchSampler) | |||
state = _get_re_batchsampler.state_dict() | |||
# 2. 断点重训,重新生成一个 dataloader; | |||
# 不改变 batch_size; | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size, shuffle=True) | |||
re_batchsampler = ReproducibleBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
re_batchsampler.load_state_dict(state) | |||
dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
# 先把这一轮的数据过完; | |||
pre_index_list = dataloader.batch_sampler.state_dict()["index_list"] | |||
while True: | |||
try: | |||
all_supposed_data.extend(next(iter_dataloader).tolist()) | |||
except StopIteration: | |||
break | |||
assert all_supposed_data == list(pre_index_list) | |||
# 重新开启新的一轮; | |||
for _ in range(3): | |||
iter_dataloader = iter(dataloader) | |||
res = [] | |||
while True: | |||
try: | |||
res.append(next(iter_dataloader)) | |||
except StopIteration: | |||
break | |||
def test_3(self): | |||
import torch | |||
from torch.utils.data import DataLoader, RandomSampler, BatchSampler | |||
before_batch_size = 7 | |||
dataset = TorchNormalDataset(num_of_data=100) | |||
# 开启 shuffle,来检验断点重训后的第二轮的 index list 是不是重新生成的; | |||
dataloader = DataLoader(dataset, batch_size=before_batch_size) | |||
for idx, data in enumerate(dataloader): | |||
if idx > 3: | |||
break | |||
iterator = iter(dataloader) | |||
for each in iterator: | |||
pass |