@@ -65,12 +65,16 @@ def _get_backend() -> str: | |||
return catch_backend[0] | |||
# 方式 (2) | |||
for backend in CHECK_BACKEND: | |||
if backend in sys.modules: | |||
logger.debug(f"sys.modules contains backend:{catch_backend[0]}.") | |||
return backend | |||
for key, module in sys.modules.items(): | |||
catch_backend = _check_module(module) | |||
if catch_backend: | |||
break | |||
if len(catch_backend): | |||
logger.debug(f"Find a file named:{catch_backend[1]} from sys.modules contains backend:{catch_backend[0]}.") | |||
logger.debug(f"Find a module file named:{catch_backend[1]} from sys.modules contains backend:{catch_backend[0]}.") | |||
return catch_backend[0] | |||
return 'numpy' | |||
@@ -227,7 +231,7 @@ class Collator: | |||
设置可以 pad 的 field 默认 pad 为什么类型的 tensor | |||
:param backend: 对于可以 pad 的 field,使用哪种 tensor,支持 ['torch','jittor','paddle','numpy','raw', 'auto', None], | |||
若为 auto ,则在进行 pad 的时候会根据调用的环境决定其 backend 。 | |||
若为 auto ,则在进行 pad 的时候会自动根据调用的环境决定其 backend 。 | |||
:return: | |||
""" | |||
assert backend in SUPPORTED_BACKENDS | |||
@@ -74,7 +74,7 @@ def _get_dtype(ele_dtype, dtype, class_name): | |||
elif is_numpy_generic_class(ele_dtype): | |||
dtype = numpy_to_paddle_dtype_dict.get(ele_dtype) | |||
else: | |||
dtype == ele_dtype | |||
dtype = ele_dtype | |||
return dtype | |||
@@ -174,6 +174,5 @@ def get_padded_paddle_tensor(batch_field, dtype=None, pad_val=0): | |||
""" | |||
shapes = get_shape(batch_field) | |||
tensor = paddle.to_tensor(np.full(shape=shapes, fill_value=pad_val), dtype=dtype) | |||
# tensor = paddle.full(shape=shapes, dtype=dtype, fill_value=pad_val) | |||
tensor = fill_tensor(batch_field, tensor, dtype=dtype) | |||
return tensor |
@@ -363,7 +363,6 @@ class Trainer(TrainerEventTrigger): | |||
raise e | |||
finally: | |||
self.on_train_end() | |||
self.driver.barrier() | |||
def _set_num_eval_batch_per_dl(self, num_eval_batch_per_dl): | |||
def _evaluate_fn(trainer: Trainer, evaluate_fn: Callable) -> None: | |||
@@ -441,6 +440,7 @@ class Trainer(TrainerEventTrigger): | |||
""" | |||
_own_callbacks: List = copy.deepcopy(self._custom_callbacks["all"]) | |||
_own_callbacks.extend(self._custom_callbacks[None]) | |||
logger.debug(f"Get {len(_own_callbacks)} callback fns through Trainer.on().") | |||
self._custom_callbacks[None] = [] | |||
if self.marker is not None: | |||
if len(self._custom_callbacks[self.marker]) == 0: | |||
@@ -14,7 +14,7 @@ else: | |||
from fastNLP.core.dataset import DataSet as Dataset | |||
from fastNLP.core.utils.jittor_utils import jittor_collate_wraps | |||
from fastNLP.core.collators import Collator | |||
from fastNLP.core.utils.utils import indice_collate_wrapper | |||
from fastNLP.core.dataloaders.utils import indice_collate_wrapper | |||
from fastNLP.core.dataset import DataSet as FDataSet | |||
@@ -106,33 +106,33 @@ class JittorDataLoader: | |||
return len(self.dataset) // self.dataset.batch_size | |||
return (len(self.dataset) - 1) // self.dataset.batch_size + 1 | |||
def set_pad(self, field_name: Union[str, tuple], pad_val: Union[int, float, None] = 0, dtype=None, backend=None, | |||
pad_fn: Callable = None) -> "JittorDataLoader": | |||
def set_pad(self, field_name:Union[str, tuple], pad_val:Union[int, float, None]=0, dtype=None, backend=None, | |||
pad_fn:Callable=None) -> Collator: | |||
""" | |||
如果需要对某个 field 的内容进行特殊的调整,请使用这个函数。 | |||
:param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的,则可以直接使用对应的 | |||
field 的 key 来表示,如果是 nested 的 dict,可以使用元组表示多层次的 key,例如 {'a': {'b': 1}} 中的使用 ('a', 'b'); | |||
如果 __getitem__ 返回的是 Sequence 类型的,则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没 | |||
有找到,则报错;如果 __getitem__ 返回的是就是整体内容,请使用 "_single" 。 | |||
:param pad_val: 这个 field 的默认 pad 值。如果设置为 None,则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的 | |||
field 进行 pad,所以如果对应 field 本身就不是可以 pad 的形式,可以不需要主动设置为 None 。 | |||
:param dtype: 对于需要 pad 的 field ,该 field 的数据 dtype 应该是什么。 | |||
:param backend: 可选[None, 'numpy', 'torch', 'paddle', 'jittor'],分别代表,输出为 list, numpy.ndarray, torch.Tensor, | |||
paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ,该值只能为 None 或 numpy 。 | |||
:param pad_fn: 指定当前 field 的 pad 函数,传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的 | |||
batch 形式。 Collator 将自动 unbatch 数据,然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch | |||
形式,输出将被直接作为结果输出。 | |||
:return: 返回 Collator 自身 | |||
如果需要对某个 field 的内容进行特殊的调整,请使用这个函数。 | |||
:param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的,则可以直接使用对应的 | |||
field 的 key 来表示,如果是 nested 的 dict,可以使用元组表示多层次的 key,例如 {'a': {'b': 1}} 中的使用 ('a', 'b'); | |||
如果 __getitem__ 返回的是 Sequence 类型的,则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没 | |||
有找到,则报错;如果 __getitem__ 返回的是就是整体内容,请使用 "_single" 。 | |||
:param pad_val: 这个 field 的默认 pad 值。如果设置为 None,则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的 | |||
field 进行 pad,所以如果对应 field 本身就不是可以 pad 的形式,可以不需要主动设置为 None 。如果 backend 为 None ,该值 | |||
无意义。 | |||
:param dtype: 对于需要 pad 的 field ,该 field 的数据 dtype 应该是什么。 | |||
:param backend: 可选['raw', 'numpy', 'torch', 'paddle', 'jittor', 'auto'],分别代表,输出为 list, numpy.ndarray, | |||
torch.Tensor, paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ,该值无意义 。 | |||
:param pad_fn: 指定当前 field 的 pad 函数,传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的 | |||
batch 形式。 Collator 将自动 unbatch 数据,然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch | |||
形式,输出将被直接作为结果输出。 | |||
:return: 返回 Collator 自身 | |||
""" | |||
if isinstance(self._collate_fn, Collator): | |||
self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, | |||
backend=backend) | |||
return self | |||
self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, backend=backend) | |||
return self._collate_fn | |||
else: | |||
raise ValueError(f"collate_fn is not fastnlp collator") | |||
raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_pad() is allowed.") | |||
def set_ignore(self, *field_names) -> "JittorDataLoader": | |||
def set_ignore(self, *field_names) -> Collator: | |||
""" | |||
如果有的内容不希望输出,可以在此处进行设置,被设置的 field 将在 batch 的输出中被忽略。 | |||
Ex:: | |||
@@ -145,18 +145,17 @@ class JittorDataLoader: | |||
""" | |||
if isinstance(self._collate_fn, Collator): | |||
self._collate_fn.set_ignore(*field_names) | |||
return self | |||
return self._collate_fn | |||
else: | |||
raise ValueError(f"collate_fn is not fastnlp collator") | |||
raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_ignore() is allowed.") | |||
def get_batch_indices(self) -> List[int]: | |||
""" | |||
获取当前数据的idx | |||
获取当前 batch 的 idx | |||
:return: | |||
""" | |||
return self.cur_batch_indices | |||
def prepare_jittor_dataloader(): | |||
... |
@@ -15,8 +15,9 @@ else: | |||
from fastNLP.core.utils.dummy_class import DummyClass as DataLoader | |||
from fastNLP.core.collators.collator import Collator | |||
from fastNLP.core.utils.utils import indice_collate_wrapper | |||
from fastNLP.core.dataloaders.utils import indice_collate_wrapper | |||
from fastNLP.core.dataset import DataSet as FDataSet | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, RandomBatchSampler | |||
class _PaddleDataset(Dataset): | |||
@@ -54,6 +55,10 @@ class PaddleDataLoader(DataLoader): | |||
if not isinstance(dataset, _PaddleDataset): | |||
dataset = _PaddleDataset(dataset) | |||
if batch_sampler is None: | |||
batch_sampler = RandomBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle, | |||
drop_last=drop_last) | |||
super(PaddleDataLoader, self).__init__(dataset=dataset, feed_list=feed_list, places=places, | |||
return_list=return_list, batch_sampler=batch_sampler, | |||
batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, | |||
@@ -66,8 +71,6 @@ class PaddleDataLoader(DataLoader): | |||
if isinstance(dataset.dataset, FDataSet): | |||
self._collate_fn = dataset.dataset.collator | |||
self._collate_fn.set_backend(backend="paddle") | |||
# if collate_fn is not None: | |||
# self._collate_fn.add_collator(collate_fn) | |||
else: | |||
self._collate_fn = Collator(backend="paddle") | |||
@@ -94,33 +97,33 @@ class PaddleDataLoader(DataLoader): | |||
self.cur_batch_indices = indices | |||
yield data | |||
def set_pad(self, field_name: Union[str, tuple], pad_val: Union[int, float, None] = 0, dtype=None, backend=None, | |||
pad_fn: Callable = None) -> "PaddleDataLoader": | |||
def set_pad(self, field_name:Union[str, tuple], pad_val:Union[int, float, None]=0, dtype=None, backend=None, | |||
pad_fn:Callable=None) -> Collator: | |||
""" | |||
如果需要对某个 field 的内容进行特殊的调整,请使用这个函数。 | |||
:param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的,则可以直接使用对应的 | |||
field 的 key 来表示,如果是 nested 的 dict,可以使用元组表示多层次的 key,例如 {'a': {'b': 1}} 中的使用 ('a', 'b'); | |||
如果 __getitem__ 返回的是 Sequence 类型的,则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没 | |||
有找到,则报错;如果 __getitem__ 返回的是就是整体内容,请使用 "_single" 。 | |||
:param pad_val: 这个 field 的默认 pad 值。如果设置为 None,则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的 | |||
field 进行 pad,所以如果对应 field 本身就不是可以 pad 的形式,可以不需要主动设置为 None 。 | |||
:param dtype: 对于需要 pad 的 field ,该 field 的数据 dtype 应该是什么。 | |||
:param backend: 可选[None, 'numpy', 'torch', 'paddle', 'jittor'],分别代表,输出为 list, numpy.ndarray, torch.Tensor, | |||
paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ,该值只能为 None 或 numpy 。 | |||
:param pad_fn: 指定当前 field 的 pad 函数,传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的 | |||
batch 形式。 Collator 将自动 unbatch 数据,然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch | |||
形式,输出将被直接作为结果输出。 | |||
:return: 返回 Collator 自身 | |||
如果需要对某个 field 的内容进行特殊的调整,请使用这个函数。 | |||
:param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的,则可以直接使用对应的 | |||
field 的 key 来表示,如果是 nested 的 dict,可以使用元组表示多层次的 key,例如 {'a': {'b': 1}} 中的使用 ('a', 'b'); | |||
如果 __getitem__ 返回的是 Sequence 类型的,则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没 | |||
有找到,则报错;如果 __getitem__ 返回的是就是整体内容,请使用 "_single" 。 | |||
:param pad_val: 这个 field 的默认 pad 值。如果设置为 None,则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的 | |||
field 进行 pad,所以如果对应 field 本身就不是可以 pad 的形式,可以不需要主动设置为 None 。如果 backend 为 None ,该值 | |||
无意义。 | |||
:param dtype: 对于需要 pad 的 field ,该 field 的数据 dtype 应该是什么。 | |||
:param backend: 可选['raw', 'numpy', 'torch', 'paddle', 'jittor', 'auto'],分别代表,输出为 list, numpy.ndarray, | |||
torch.Tensor, paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ,该值无意义 。 | |||
:param pad_fn: 指定当前 field 的 pad 函数,传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的 | |||
batch 形式。 Collator 将自动 unbatch 数据,然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch | |||
形式,输出将被直接作为结果输出。 | |||
:return: 返回 Collator 自身 | |||
""" | |||
if isinstance(self._collate_fn, Collator): | |||
self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, | |||
backend=backend) | |||
return self | |||
self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, backend=backend) | |||
return self._collate_fn | |||
else: | |||
raise ValueError(f"collate_fn is not fastnlp collator") | |||
raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_pad() is allowed.") | |||
def set_ignore(self, *field_names) -> "PaddleDataLoader": | |||
def set_ignore(self, *field_names) -> Collator: | |||
""" | |||
如果有的内容不希望输出,可以在此处进行设置,被设置的 field 将在 batch 的输出中被忽略。 | |||
Ex:: | |||
@@ -133,13 +136,13 @@ class PaddleDataLoader(DataLoader): | |||
""" | |||
if isinstance(self._collate_fn, Collator): | |||
self._collate_fn.set_ignore(*field_names) | |||
return self | |||
return self._collate_fn | |||
else: | |||
raise ValueError(f"collate_fn is not fastnlp collator") | |||
raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_ignore() is allowed.") | |||
def get_batch_indices(self) -> List[int]: | |||
""" | |||
获取当前数据的idx | |||
获取当前 batch 的 idx | |||
:return: | |||
""" | |||
@@ -147,7 +150,8 @@ class PaddleDataLoader(DataLoader): | |||
def prepare_paddle_dataloader(ds_or_db, feed_list=None, places=None, | |||
return_list: bool = True, batch_sampler=None, | |||
return_list: bool = True, | |||
batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None, | |||
train_batch_size: int = 1, shuffle: bool = False, | |||
drop_last: bool = False, collate_fn: Union[Callable, str, None] = None, | |||
num_workers: int = 0, use_buffer_reader: bool = True, | |||
@@ -3,14 +3,14 @@ __all__ = [ | |||
'prepare_torch_dataloader' | |||
] | |||
from typing import Optional, Callable, Sequence, List, Union, Tuple, Dict, Mapping | |||
from typing import Optional, Callable, Sequence, Union, Tuple, Dict, Mapping | |||
from fastNLP.core.dataset import DataSet | |||
from fastNLP.core.collators import Collator | |||
from fastNLP.core.utils.utils import indice_collate_wrapper | |||
from fastNLP.core.dataloaders.utils import indice_collate_wrapper | |||
from fastNLP.io.data_bundle import DataBundle | |||
from fastNLP.envs.imports import _NEED_IMPORT_TORCH | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, UnrepeatedSampler | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, UnrepeatedSampler, RandomSampler | |||
if _NEED_IMPORT_TORCH: | |||
from torch.utils.data import DataLoader, Sampler | |||
@@ -76,6 +76,9 @@ class TorchDataLoader(DataLoader): | |||
if not isinstance(dataset, _FDataSet): | |||
dataset = _FDataSet(dataset) | |||
if sampler is None and batch_sampler is None: | |||
sampler = RandomSampler(dataset, shuffle=shuffle) | |||
super().__init__(dataset=dataset, batch_size=batch_size, shuffle=shuffle, sampler=sampler, | |||
batch_sampler=batch_sampler, num_workers=num_workers, collate_fn=None, | |||
pin_memory=pin_memory, drop_last=drop_last, timeout=timeout, worker_init_fn=worker_init_fn, | |||
@@ -87,9 +90,6 @@ class TorchDataLoader(DataLoader): | |||
if isinstance(dataset.dataset, DataSet): # 使用了 fastnlp dataset | |||
self._collate_fn = dataset.dataset.collator | |||
self._collate_fn.set_backend(backend="torch") | |||
# if collate_fn is not None and collate_fn is not default_collate: | |||
# # 防止ddp重新初始化时候将torch dataloader的默认collate加进来 | |||
# self._collate_fn.add_collator(collate_fn) | |||
else: | |||
self._collate_fn = Collator(backend="torch") | |||
else: | |||
@@ -112,31 +112,32 @@ class TorchDataLoader(DataLoader): | |||
yield data | |||
def set_pad(self, field_name:Union[str, tuple], pad_val:Union[int, float, None]=0, dtype=None, backend=None, | |||
pad_fn:Callable=None) -> "TorchDataLoader": | |||
pad_fn:Callable=None) -> Collator: | |||
""" | |||
如果需要对某个 field 的内容进行特殊的调整,请使用这个函数。 | |||
如果需要对某个 field 的内容进行特殊的调整,请使用这个函数。 | |||
:param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的,则可以直接使用对应的 | |||
field 的 key 来表示,如果是 nested 的 dict,可以使用元组表示多层次的 key,例如 {'a': {'b': 1}} 中的使用 ('a', 'b'); | |||
如果 __getitem__ 返回的是 Sequence 类型的,则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没 | |||
有找到,则报错;如果 __getitem__ 返回的是就是整体内容,请使用 "_single" 。 | |||
:param pad_val: 这个 field 的默认 pad 值。如果设置为 None,则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的 | |||
field 进行 pad,所以如果对应 field 本身就不是可以 pad 的形式,可以不需要主动设置为 None 。 | |||
:param dtype: 对于需要 pad 的 field ,该 field 的数据 dtype 应该是什么。 | |||
:param backend: 可选[None, 'numpy', 'torch', 'paddle', 'jittor'],分别代表,输出为 list, numpy.ndarray, torch.Tensor, | |||
paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ,该值只能为 None 或 numpy 。 | |||
:param pad_fn: 指定当前 field 的 pad 函数,传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的 | |||
batch 形式。 Collator 将自动 unbatch 数据,然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch | |||
形式,输出将被直接作为结果输出。 | |||
:return: 返回 Collator 自身 | |||
:param field_name: 需要调整的 field 的名称。如果 Dataset 的 __getitem__ 方法返回的是 dict 类型的,则可以直接使用对应的 | |||
field 的 key 来表示,如果是 nested 的 dict,可以使用元组表示多层次的 key,例如 {'a': {'b': 1}} 中的使用 ('a', 'b'); | |||
如果 __getitem__ 返回的是 Sequence 类型的,则可以使用 '_0', '_1' 表示序列中第 0 或 1 个元素。如果该 field 在数据中没 | |||
有找到,则报错;如果 __getitem__ 返回的是就是整体内容,请使用 "_single" 。 | |||
:param pad_val: 这个 field 的默认 pad 值。如果设置为 None,则表示该 field 不需要 pad , fastNLP 默认只会对可以 pad 的 | |||
field 进行 pad,所以如果对应 field 本身就不是可以 pad 的形式,可以不需要主动设置为 None 。如果 backend 为 None ,该值 | |||
无意义。 | |||
:param dtype: 对于需要 pad 的 field ,该 field 的数据 dtype 应该是什么。 | |||
:param backend: 可选['raw', 'numpy', 'torch', 'paddle', 'jittor', 'auto'],分别代表,输出为 list, numpy.ndarray, | |||
torch.Tensor, paddle.Tensor, jittor.Var 类型。若 pad_val 为 None ,该值无意义 。 | |||
:param pad_fn: 指定当前 field 的 pad 函数,传入该函数则 pad_val, dtype, backend 等参数失效。pad_fn 的输入为当前 field 的 | |||
batch 形式。 Collator 将自动 unbatch 数据,然后将各个 field 组成各自的 batch 。pad_func 的输入即为 field 的 batch | |||
形式,输出将被直接作为结果输出。 | |||
:return: 返回 Collator 自身 | |||
""" | |||
if isinstance(self._collate_fn, Collator): | |||
self._collate_fn.set_pad(field_name=field_name, pad_val=pad_val, dtype=dtype, pad_fn=pad_fn, backend=backend) | |||
return self | |||
return self._collate_fn | |||
else: | |||
raise ValueError(f"collate_fn is not fastnlp collator") | |||
raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_pad() is allowed.") | |||
def set_ignore(self, *field_names) -> "TorchDataLoader": | |||
def set_ignore(self, *field_names) -> Collator: | |||
""" | |||
如果有的内容不希望输出,可以在此处进行设置,被设置的 field 将在 batch 的输出中被忽略。 | |||
Ex:: | |||
@@ -149,24 +150,15 @@ class TorchDataLoader(DataLoader): | |||
""" | |||
if isinstance(self._collate_fn, Collator): | |||
self._collate_fn.set_ignore(*field_names) | |||
return self | |||
return self._collate_fn | |||
else: | |||
raise ValueError(f"collate_fn is not fastnlp collator") | |||
def get_batch_indices(self) -> List[int]: | |||
""" | |||
获取当前数据的idx | |||
:return: | |||
""" | |||
return self.cur_batch_indices | |||
raise ValueError(f"Only when the collate_fn is a fastNLP Collator, set_ignore() is allowed.") | |||
def prepare_torch_dataloader(ds_or_db: Union[DataSet, DataBundle, Sequence[DataSet], Mapping[str, DataSet]], | |||
batch_size: int = 1, | |||
shuffle: bool = False, sampler: Optional["Sampler[int]"] = None, | |||
batch_sampler: Optional["Sampler[Sequence[int]]"] = None, | |||
shuffle: bool = False, sampler: Union["Sampler[int]", ReproducibleSampler, UnrepeatedSampler] = None, | |||
batch_sampler: Union["Sampler[Sequence[int]]", ReproducibleBatchSampler] = None, | |||
num_workers: int = 0, collate_fn: Union[str, Callable, None] = None, | |||
pin_memory: bool = False, drop_last: bool = False, | |||
timeout: float = 0, worker_init_fn: Optional[Callable] = None, | |||
@@ -0,0 +1,16 @@ | |||
def indice_collate_wrapper(func): | |||
""" | |||
其功能是封装一层collate_fn,将dataset取到的tuple数据分离开,将idx打包为indices。 | |||
:param func: 需要修饰的函数 | |||
:return: | |||
""" | |||
def wrapper(tuple_data): | |||
indice, ins_list = [], [] | |||
for idx, ins in tuple_data: | |||
indice.append(idx) | |||
ins_list.append(ins) | |||
return indice, func(ins_list) | |||
return wrapper |
@@ -780,7 +780,7 @@ class DataSet: | |||
self.collator.set_ignore(*field_names) | |||
@property | |||
def collator(self): | |||
def collator(self) -> Collator: | |||
if self._collator is None: | |||
self._collator = Collator() | |||
return self._collator |
@@ -22,7 +22,7 @@ from fastNLP.core.utils import ( | |||
rank_zero_rm | |||
) | |||
from fastNLP.core.samplers import ( | |||
RandomBatchSampler, | |||
ReproduceBatchSampler, | |||
ReproducibleSampler, | |||
ReproducibleBatchSampler, | |||
RandomSampler, | |||
@@ -485,7 +485,7 @@ class PaddleFleetDriver(PaddleDriver): | |||
return self.model, model.forward | |||
def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler, RandomBatchSampler]], | |||
def set_dist_repro_dataloader(self, dataloader, dist: Optional[Union[str, ReproducibleSampler, ReproduceBatchSampler]], | |||
reproducible: bool = False): | |||
r""" | |||
根据输入的 dataloader 得到一个 支持分布式 (distributed) 与 可复现的 (reproducible) 的 dataloader。 | |||
@@ -22,7 +22,7 @@ from fastNLP.core.log import logger | |||
from fastNLP.core.samplers import ( | |||
ReproducibleBatchSampler, | |||
ReproducibleSampler, | |||
RandomBatchSampler, | |||
ReproduceBatchSampler, | |||
RandomSampler, | |||
) | |||
@@ -345,7 +345,7 @@ class PaddleDriver(Driver): | |||
raise RuntimeError("It is not allowed to use checkpoint retraining when you do not use our or " | |||
"`ReproducibleSampler`.") | |||
else: | |||
sampler = RandomBatchSampler( | |||
sampler = ReproduceBatchSampler( | |||
batch_sampler=dataloader_args.batch_sampler if dataloader_args.batch_sampler is not None else dataloader_args.sampler, | |||
batch_size=dataloader_args.batch_size, | |||
drop_last=dataloader_args.drop_last | |||
@@ -476,7 +476,7 @@ class PaddleDriver(Driver): | |||
res.shuffle = True | |||
else: | |||
res.shuffle = False | |||
# RandomBatchSampler 的情况 | |||
# ReproduceBatchSampler 的情况 | |||
elif hasattr(dataloader.batch_sampler, "batch_sampler"): | |||
batch_sampler = dataloader.batch_sampler.batch_sampler | |||
res.sampler = batch_sampler.sampler | |||
@@ -14,7 +14,7 @@ from fastNLP.core.utils import ( | |||
from fastNLP.core.utils.utils import _get_fun_msg | |||
from fastNLP.core.samplers import ( | |||
ReproducibleBatchSampler, | |||
RandomBatchSampler, | |||
ReproduceBatchSampler, | |||
ReproducibleSampler, | |||
RandomSampler, | |||
re_instantiate_sampler, | |||
@@ -177,7 +177,7 @@ class PaddleSingleDriver(PaddleDriver): | |||
logger.debug("Replace paddle RandomSampler into fastNLP RandomSampler.") | |||
return replace_sampler(dataloader, sampler) | |||
else: | |||
batch_sampler = RandomBatchSampler( | |||
batch_sampler = ReproduceBatchSampler( | |||
batch_sampler=args.batch_sampler, | |||
batch_size=args.batch_size, | |||
drop_last=args.drop_last | |||
@@ -15,7 +15,7 @@ from .torch_driver import TorchDriver | |||
from fastNLP.core.drivers.torch_driver.utils import replace_sampler, replace_batch_sampler | |||
from fastNLP.core.utils import auto_param_call | |||
from fastNLP.core.utils.utils import _get_fun_msg | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler, RandomBatchSampler | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, re_instantiate_sampler, ReproduceBatchSampler | |||
from fastNLP.core.samplers import RandomSampler | |||
from fastNLP.core.log import logger | |||
@@ -113,7 +113,7 @@ class TorchSingleDriver(TorchDriver): | |||
logger.debug("Replace torch RandomSampler into fastNLP RandomSampler.") | |||
return replace_sampler(dataloader, sampler) | |||
else: | |||
batch_sampler = RandomBatchSampler( | |||
batch_sampler = ReproduceBatchSampler( | |||
batch_sampler=args.batch_sampler, | |||
batch_size=args.batch_size, | |||
drop_last=args.drop_last | |||
@@ -31,7 +31,7 @@ from fastNLP.core.utils import apply_to_collection, torch_move_data_to_device | |||
from fastNLP.envs import rank_zero_call | |||
from fastNLP.envs import FASTNLP_SEED_WORKERS, FASTNLP_GLOBAL_RANK, FASTNLP_MODEL_FILENAME, FASTNLP_CHECKPOINT_FILENAME | |||
from fastNLP.core.log import logger | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, RandomBatchSampler, RandomSampler | |||
from fastNLP.core.samplers import ReproducibleBatchSampler, ReproducibleSampler, ReproduceBatchSampler, RandomSampler | |||
class TorchDriver(Driver): | |||
@@ -293,7 +293,7 @@ class TorchDriver(Driver): | |||
raise RuntimeError("It is not allowed to use checkpoint retraining when you do not use our or " | |||
"`ReproducibleSampler`.") | |||
else: | |||
sampler = RandomBatchSampler( | |||
sampler = ReproduceBatchSampler( | |||
batch_sampler=dataloader_args.batch_sampler if dataloader_args.batch_sampler is not None else dataloader_args.sampler, | |||
batch_size=dataloader_args.batch_size, | |||
drop_last=dataloader_args.drop_last | |||
@@ -407,7 +407,7 @@ class TorchDriver(Driver): | |||
res.shuffle = True | |||
else: | |||
res.shuffle = False | |||
# RandomBatchSampler 的情况 | |||
# ReproduceBatchSampler 的情况 | |||
elif hasattr(dataloader.batch_sampler, "batch_sampler"): | |||
batch_sampler = dataloader.batch_sampler.batch_sampler | |||
res.sampler = batch_sampler.sampler | |||
@@ -14,9 +14,10 @@ __all__ = [ | |||
"UnrepeatedSortedSampler", | |||
"UnrepeatedSequentialSampler", | |||
"RandomBatchSampler", | |||
"ReproduceBatchSampler", | |||
"BucketedBatchSampler", | |||
"ReproducibleBatchSampler", | |||
"RandomBatchSampler", | |||
"re_instantiate_sampler" | |||
] | |||
@@ -26,5 +27,5 @@ from .mix_sampler import MixSampler, DopedSampler, MixSequentialSampler, Polling | |||
from .reproducible_sampler import ReproducibleSampler, RandomSampler, SequentialSampler, SortedSampler | |||
from .utils import re_instantiate_sampler | |||
from .conversion_utils import conversion_between_reproducible_and_unrepeated_sampler | |||
from .reproducible_batch_sampler import RandomBatchSampler, BucketedBatchSampler, ReproducibleBatchSampler | |||
from .reproducible_batch_sampler import ReproduceBatchSampler, BucketedBatchSampler, ReproducibleBatchSampler, RandomBatchSampler | |||
@@ -1,5 +1,6 @@ | |||
__all__ = [ | |||
'BucketedBatchSampler', | |||
"ReproduceBatchSampler", | |||
"RandomBatchSampler" | |||
] | |||
@@ -54,13 +55,13 @@ class ReproducibleBatchSampler: | |||
raise NotImplementedError("Each specific batch_sampler should implement its own `batch_idx_in_epoch` property.") | |||
class RandomBatchSampler(ReproducibleBatchSampler): | |||
class ReproduceBatchSampler(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。RandomBatchSampler 将首先遍历一边该对象,然后将迭代 | |||
:param batch_sampler: 可迭代出 数字 或 数字列表 的可迭代对象。ReproduceBatchSampler 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。 | |||
@@ -143,7 +144,7 @@ class RandomBatchSampler(ReproducibleBatchSampler): | |||
self.need_reinitialize = False | |||
def set_distributed(self, num_replicas, rank, pad=True): | |||
raise RuntimeError(f"RandomBatchSampler does not support to change to distributed training.") | |||
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): | |||
@@ -158,6 +159,211 @@ class RandomBatchSampler(ReproducibleBatchSampler): | |||
(len(self.index_list) - self.num_consumed_samples + self.batch_size - 1) // self.batch_size | |||
class RandomBatchSampler(ReproducibleBatchSampler): | |||
def __init__(self, dataset, batch_size:int = 32, shuffle: bool = True, | |||
drop_last: bool = False, seed: int = 0, **kwargs): | |||
""" | |||
随机分 batch 的 batch_sampler 。 | |||
:param dataset: 实现了 __len__ 方法的数据容器。 | |||
:param batch_size: 每个 batch 的大小 | |||
: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__() | |||
self.dataset = dataset | |||
self.batch_size = batch_size | |||
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) | |||
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 | |||
return self | |||
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 | |||
indices = list(range(len(self.dataset))) | |||
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): | |||
_indices = indices[_i:len(indices):self.old_num_replicas] | |||
__batches = self.batchify(_indices, self.old_batch_size, seed=self.seed + self.epoch) | |||
_batches.append(__batches) | |||
batches = list(chain(*[_ for _ in zip(*_batches)])) | |||
indices = list(chain(*batches)) | |||
indices = indices[self.num_consumed_samples:] | |||
# 取出这个 rank , | |||
indices = indices[self.rank:len(indices):self.num_replicas] | |||
batches = self.batchify(indices, self.batch_size, seed=self.seed + self.epoch) | |||
batches = list(map(list, batches)) | |||
else: | |||
indices = indices[self.num_consumed_samples:] | |||
indices = indices[self.rank:len(indices):self.num_replicas] | |||
_num_batches = len(indices) // self.batch_size | |||
if _num_batches == 0: | |||
batches = [indices] | |||
else: | |||
batches = list(map(list, np.array_split(indices[:_num_batches*self.batch_size], _num_batches))) | |||
if len(indices)%self.batch_size!=0: | |||
batches.append(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 sum(map(len, 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_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 batchify(self, indices, batch_size, seed): | |||
""" | |||
将 indices 分为 batches | |||
:param sorted_indices: List[int] | |||
:param batch_size: int | |||
:param seed: int | |||
:return: List[List[int]] | |||
""" | |||
# 实际的 bucket 大小 | |||
rng = np.random.default_rng(abs(seed)) | |||
rng.shuffle(indices) | |||
num_samples = 0 | |||
batches = [] | |||
while num_samples<len(indices): | |||
batches.append(indices[num_samples:num_samples+batch_size]) | |||
num_samples += batch_size | |||
return batches | |||
def set_epoch(self, epoch): | |||
self.epoch = epoch | |||
@property | |||
def batch_idx_in_epoch(self): | |||
if self.drop_last: | |||
return len(self.dataset) // self.num_replicas // self.batch_size - self.num_left_samples // self.batch_size | |||
else: | |||
return (len(self.dataset) // self.num_replicas + self.batch_size - 1) // self.batch_size - \ | |||
(self.num_left_samples + self.batch_size - 1) // self.batch_size | |||
@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)->int: | |||
""" | |||
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 state_dict(self) -> Dict: | |||
if self.old_batch_size != self.batch_size: | |||
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, | |||
'sampler_type': self.__class__.__name__, 'length': len(self.dataset), 'shuffle': self.shuffle, | |||
'batch_size': self.batch_size, | |||
'num_replicas': self.num_replicas} | |||
return states | |||
def load_state_dict(self, states: Dict): | |||
# 如果 self.during_iter 是 True,那么 num_consumed_samples 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_replicas = states['num_replicas'] | |||
class BucketedBatchSampler(ReproducibleBatchSampler): | |||
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): | |||
@@ -54,13 +54,12 @@ class RandomSampler(ReproducibleSampler): | |||
def __init__(self, dataset, shuffle: bool = True, seed: int = 0, **kwargs): | |||
""" | |||
:param dataset: 实现了 __len__ 方法的数据容器 | |||
:param shuffle: 是否在每次 iterate 的时候打乱顺序。 | |||
:param seed: 随机数种子。 | |||
:param kwargs: 用户不需要使用,fastNLP 内部使用 | |||
""" | |||
super(RandomSampler, self).__init__() | |||
self.dataset = dataset | |||
self.shuffle = shuffle | |||
self.seed = seed | |||
@@ -21,7 +21,6 @@ __all__ = [ | |||
'nullcontext', | |||
'pretty_table_printer', | |||
'Option', | |||
'indice_collate_wrapper', | |||
'deprecated', | |||
'seq_len_to_mask', | |||
'rank_zero_rm', | |||
@@ -37,6 +36,7 @@ from .torch_paddle_utils import torch_paddle_move_data_to_device | |||
from .torch_utils import torch_move_data_to_device | |||
from .utils import get_fn_arg_names, auto_param_call, check_user_specific_params, \ | |||
dataclass_to_dict, match_and_substitute_params, apply_to_collection, nullcontext, pretty_table_printer, Option, \ | |||
indice_collate_wrapper, deprecated, seq_len_to_mask, rank_zero_rm, rank_zero_mkdir | |||
deprecated, seq_len_to_mask, rank_zero_rm, rank_zero_mkdir | |||
from ..dataloaders.utils import indice_collate_wrapper | |||
@@ -6,7 +6,7 @@ import warnings | |||
from dataclasses import is_dataclass | |||
from copy import deepcopy | |||
from collections import defaultdict, OrderedDict | |||
from typing import Callable, List, Any, Dict, AnyStr, Union, Mapping, Sequence, Optional | |||
from typing import Callable, List, Any, Dict, AnyStr, Union, Mapping, Sequence | |||
from typing import Tuple, Optional | |||
from time import sleep | |||
@@ -35,7 +35,6 @@ __all__ = [ | |||
'nullcontext', | |||
'pretty_table_printer', | |||
'Option', | |||
'indice_collate_wrapper', | |||
'deprecated', | |||
'seq_len_to_mask', | |||
'rank_zero_rm', | |||
@@ -513,24 +512,6 @@ class Option(dict): | |||
self.update(state) | |||
def indice_collate_wrapper(func): | |||
""" | |||
其功能是封装一层collate_fn,将dataset取到的tuple数据分离开,将idx打包为indices。 | |||
:param func: 需要修饰的函数 | |||
:return: | |||
""" | |||
def wrapper(tuple_data): | |||
indice, ins_list = [], [] | |||
for idx, ins in tuple_data: | |||
indice.append(idx) | |||
ins_list.append(ins) | |||
return indice, func(ins_list) | |||
return wrapper | |||
_emitted_deprecation_warnings = set() | |||
@@ -2,7 +2,7 @@ import pytest | |||
from pathlib import Path | |||
from fastNLP.core.drivers.paddle_driver.single_device import PaddleSingleDriver | |||
from fastNLP.core.samplers import RandomBatchSampler, RandomSampler | |||
from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler | |||
from tests.helpers.models.paddle_model import PaddleNormalModel_Classification_1 | |||
from tests.helpers.datasets.paddle_data import PaddleNormalDataset, PaddleRandomMaxDataset | |||
from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
@@ -278,7 +278,7 @@ class TestPaddleDriverFunctions: | |||
dataset = PaddleNormalDataset() | |||
dataloader = DataLoader( | |||
dataset, | |||
batch_sampler=RandomBatchSampler( | |||
batch_sampler=ReproduceBatchSampler( | |||
BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle), | |||
batch_size, | |||
drop_last, | |||
@@ -287,7 +287,7 @@ class TestPaddleDriverFunctions: | |||
res = PaddleSingleDriver.get_dataloader_args(dataloader) | |||
assert isinstance(res.dataset, PaddleNormalDataset) | |||
assert isinstance(res.batch_sampler, RandomBatchSampler) | |||
assert isinstance(res.batch_sampler, ReproduceBatchSampler) | |||
if shuffle: | |||
assert isinstance(res.sampler, paddle.io.RandomSampler) | |||
else: | |||
@@ -387,7 +387,7 @@ class TestSetDistReproDataloader: | |||
""" | |||
测试 set_dist_repro_dataloader 参数 `reproducible` 为 True 时的表现 | |||
当dist为字符串时,此时应该返回新的 dataloader,且如果原 sampler 为 paddle.io.RandomSampler(shuffle=True), | |||
只会替换 Sampler 为 RandomSampler;否则会替换 batch_sampler 为 RandomBatchSampler | |||
只会替换 Sampler 为 RandomSampler;否则会替换 batch_sampler 为 ReproduceBatchSampler | |||
""" | |||
dataloader = DataLoader(self.dataset, batch_size=2, shuffle=shuffle) | |||
replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=True) | |||
@@ -400,7 +400,7 @@ class TestSetDistReproDataloader: | |||
assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler) | |||
else: | |||
# 此时会替换 batch_sampler | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler.batch_sampler, BatchSampler) | |||
assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size | |||
assert replaced_loader.drop_last == dataloader.drop_last | |||
@@ -414,11 +414,11 @@ class TestSetDistReproDataloader: | |||
应该返回新的 dataloader,并将 batch_sampler 替换为 dist 对应的 Sampler | |||
""" | |||
dataloader = DataLoader(self.dataset, batch_size=2, shuffle=not shuffle) | |||
dist = RandomBatchSampler(BatchSampler(self.dataset, batch_size=4, shuffle=shuffle), 4, False) | |||
dist = ReproduceBatchSampler(BatchSampler(self.dataset, batch_size=4, shuffle=shuffle), 4, False) | |||
replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist=dist, reproducible=False) | |||
assert not (replaced_loader is dataloader) | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert replaced_loader.batch_sampler is dist | |||
self.check_set_dist_repro_dataloader(dataloader, replaced_loader, shuffle) | |||
@@ -450,7 +450,7 @@ class TestSetDistReproDataloader: | |||
""" | |||
dataloader = DataLoader( | |||
dataset=self.dataset, | |||
batch_sampler=RandomBatchSampler( | |||
batch_sampler=ReproduceBatchSampler( | |||
BatchSampler(self.dataset, batch_size=4, shuffle=shuffle), | |||
batch_size=4, | |||
drop_last=False, | |||
@@ -459,7 +459,7 @@ class TestSetDistReproDataloader: | |||
replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=False) | |||
assert not (replaced_loader is dataloader) | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert not (replaced_loader.batch_sampler is dataloader.batch_sampler) | |||
assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size | |||
assert replaced_loader.drop_last == dataloader.drop_last | |||
@@ -500,20 +500,20 @@ class TestSetDistReproDataloader: | |||
if idx >= num_consumed_batches: | |||
break | |||
already_seen_idx.update(batch) | |||
if isinstance(replaced_loader.batch_sampler, RandomBatchSampler): | |||
if isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler): | |||
sampler_states = replaced_loader.batch_sampler.state_dict() | |||
else: | |||
sampler_states = replaced_loader.batch_sampler.sampler.state_dict() | |||
# 重新加载,应该可以输出剩下的内容,且对于 PaddleNormalDataset 来说,排序后应该是一个 range | |||
left_idxes = set() | |||
if isinstance(replaced_loader.batch_sampler, RandomBatchSampler): | |||
if isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler): | |||
batch_size = replaced_loader.batch_sampler.batch_size | |||
sampler_states["num_consumed_samples"] = num_consumed_batches * batch_size | |||
# 重新改造 dataloader | |||
new_loader = DataLoader( | |||
dataset=replaced_loader.dataset, | |||
batch_sampler=RandomBatchSampler( | |||
batch_sampler=ReproduceBatchSampler( | |||
BatchSampler(replaced_loader.dataset, shuffle=shuffle, batch_size=batch_size), | |||
batch_size=batch_size, | |||
drop_last=False, | |||
@@ -603,7 +603,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
dataset = PaddleRandomMaxDataset(40, 10) | |||
dataloader = DataLoader( | |||
dataset=dataset, | |||
batch_sampler=RandomBatchSampler(BatchSampler(dataset, batch_size=4), 4, False) | |||
batch_sampler=ReproduceBatchSampler(BatchSampler(dataset, batch_size=4), 4, False) | |||
) | |||
driver1, driver2 = generate_random_driver(10, 10, fp16, "gpu"), generate_random_driver(10, 10, False, "gpu") | |||
@@ -627,7 +627,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
# 更改 batch_size | |||
dataloader = DataLoader( | |||
dataset=dataset, | |||
batch_sampler=RandomBatchSampler(BatchSampler(dataset, batch_size=2, shuffle=True), 2, False) | |||
batch_sampler=ReproduceBatchSampler(BatchSampler(dataset, batch_size=2, shuffle=True), 2, False) | |||
) | |||
load_states = driver2.load(Path(path), dataloader, only_state_dict, should_load_model=True) | |||
replaced_loader = load_states.pop("dataloader") | |||
@@ -637,7 +637,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
# 2. 检查 batch_sampler 是否被正确地加载和替换 | |||
assert not (replaced_loader is dataloader) | |||
assert replaced_loader.batch_sampler is dataloader.batch_sampler | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert replaced_loader.batch_sampler.index_list == sampler_states["index_list"] | |||
assert replaced_loader.batch_sampler.num_consumed_samples == num_consumed_batches * 4 | |||
@@ -6,7 +6,7 @@ from fastNLP.core.drivers.paddle_driver.utils import ( | |||
replace_batch_sampler, | |||
replace_sampler, | |||
) | |||
from fastNLP.core.samplers import RandomBatchSampler, RandomSampler | |||
from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler | |||
from fastNLP.envs.imports import _NEED_IMPORT_PADDLE | |||
if _NEED_IMPORT_PADDLE: | |||
import paddle | |||
@@ -36,12 +36,12 @@ def test_get_device_from_visible_str(user_visible_devices, cuda_visible_devices, | |||
def test_replace_batch_sampler(): | |||
dataset = PaddleNormalDataset(10) | |||
dataloader = DataLoader(dataset, batch_size=32) | |||
batch_sampler = RandomBatchSampler(dataloader.batch_sampler, batch_size=16, drop_last=False) | |||
batch_sampler = ReproduceBatchSampler(dataloader.batch_sampler, batch_size=16, drop_last=False) | |||
replaced_loader = replace_batch_sampler(dataloader, batch_sampler) | |||
assert not (replaced_loader is dataloader) | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert isinstance(replaced_loader.dataset, PaddleNormalDataset) | |||
assert len(replaced_loader.dataset) == len(dataset) | |||
assert replaced_loader.batch_sampler.batch_size == 16 | |||
@@ -2,7 +2,7 @@ import pytest | |||
from pathlib import Path | |||
from fastNLP.core.drivers.torch_driver.single_device import TorchSingleDriver | |||
from fastNLP.core.samplers import RandomBatchSampler, RandomSampler | |||
from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler | |||
from tests.helpers.models.torch_model import TorchNormalModel_Classification_1 | |||
from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchArgMaxDataset | |||
from tests.helpers.datasets.paddle_data import PaddleNormalDataset | |||
@@ -17,7 +17,7 @@ if _NEED_IMPORT_PADDLE: | |||
def dataloader_with_randombatchsampler(dataset, batch_size, shuffle, drop_last): | |||
""" | |||
建立一个 batch_sampler 为 RandomBatchSampler 的 dataloader | |||
建立一个 batch_sampler 为 ReproduceBatchSampler 的 dataloader | |||
""" | |||
if shuffle: | |||
sampler = torch.utils.data.RandomSampler(dataset) | |||
@@ -25,7 +25,7 @@ def dataloader_with_randombatchsampler(dataset, batch_size, shuffle, drop_last): | |||
sampler = torch.utils.data.SequentialSampler(dataset) | |||
dataloader = DataLoader( | |||
dataset=dataset, | |||
batch_sampler=RandomBatchSampler( | |||
batch_sampler=ReproduceBatchSampler( | |||
BatchSampler( | |||
sampler, batch_size=batch_size, drop_last=drop_last | |||
), | |||
@@ -306,7 +306,7 @@ class TestTorchDriverFunctions: | |||
res = TorchSingleDriver.get_dataloader_args(dataloader) | |||
assert isinstance(res.dataset, TorchNormalDataset) | |||
assert isinstance(res.batch_sampler, RandomBatchSampler) | |||
assert isinstance(res.batch_sampler, ReproduceBatchSampler) | |||
if shuffle: | |||
assert isinstance(res.sampler, torch.utils.data.RandomSampler) | |||
else: | |||
@@ -401,7 +401,7 @@ class TestSetDistReproDataloader: | |||
""" | |||
测试 set_dist_repro_dataloader 参数 `reproducible` 为 True 时的表现 | |||
当dist为字符串时,此时应该返回新的 dataloader,且如果原 sampler 为 torch.utils.data.RandomSampler(shuffle=True), | |||
只会替换 Sampler 为 RandomSampler;否则会替换 batch_sampler 为 RandomBatchSampler | |||
只会替换 Sampler 为 RandomSampler;否则会替换 batch_sampler 为 ReproduceBatchSampler | |||
""" | |||
dataloader = DataLoader(self.dataset, batch_size=2, shuffle=shuffle) | |||
replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=True) | |||
@@ -414,7 +414,7 @@ class TestSetDistReproDataloader: | |||
assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler) | |||
else: | |||
# 此时会替换 batch_sampler | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler.batch_sampler, BatchSampler) | |||
assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size | |||
assert replaced_loader.drop_last == dataloader.drop_last | |||
@@ -428,11 +428,11 @@ class TestSetDistReproDataloader: | |||
应该返回新的 dataloader,并将 batch_sampler 替换为 dist 对应的 Sampler | |||
""" | |||
dataloader = DataLoader(self.dataset, batch_size=2, shuffle=shuffle) | |||
dist = RandomBatchSampler(BatchSampler(self.dataset, batch_size=4, drop_last=False), 4, False) | |||
dist = ReproduceBatchSampler(BatchSampler(self.dataset, batch_size=4, drop_last=False), 4, False) | |||
replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist=dist, reproducible=False) | |||
assert not (replaced_loader is dataloader) | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert replaced_loader.batch_sampler is dist | |||
self.check_set_dist_repro_dataloader(dataloader, replaced_loader, shuffle) | |||
@@ -466,7 +466,7 @@ class TestSetDistReproDataloader: | |||
replaced_loader = self.driver.set_dist_repro_dataloader(dataloader, dist="dist", reproducible=False) | |||
assert not (replaced_loader is dataloader) | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert not (replaced_loader.batch_sampler is dataloader.batch_sampler) | |||
assert replaced_loader.batch_sampler.batch_size == dataloader.batch_sampler.batch_size | |||
assert replaced_loader.drop_last == dataloader.drop_last | |||
@@ -502,14 +502,14 @@ class TestSetDistReproDataloader: | |||
if idx >= num_consumed_batches: | |||
break | |||
already_seen_idx.update(batch) | |||
if isinstance(replaced_loader.batch_sampler, RandomBatchSampler): | |||
if isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler): | |||
sampler_states = replaced_loader.batch_sampler.state_dict() | |||
else: | |||
sampler_states = replaced_loader.batch_sampler.sampler.state_dict() | |||
# 重新加载,应该可以输出剩下的内容,且对于 TorchNormalDataset 来说,排序后应该是一个 range | |||
left_idxes = set() | |||
if isinstance(replaced_loader.batch_sampler, RandomBatchSampler): | |||
if isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler): | |||
batch_size = replaced_loader.batch_sampler.batch_size | |||
sampler_states["num_consumed_samples"] = num_consumed_batches * batch_size | |||
# 重新改造 dataloader | |||
@@ -613,7 +613,7 @@ def test_save_and_load_with_randombatchsampler(only_state_dict, fp16): | |||
# 2. 检查 batch_sampler 是否被正确地加载和替换 | |||
assert not (replaced_loader is dataloader) | |||
assert replaced_loader.batch_sampler is dataloader.batch_sampler | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert replaced_loader.batch_sampler.index_list == sampler_states["index_list"] | |||
assert replaced_loader.batch_sampler.num_consumed_samples == num_consumed_batches * 4 | |||
@@ -30,7 +30,7 @@ class SequenceDataSet: | |||
def check_replace_sampler(driver): | |||
# dist_sampler 可以选择的有['dist', 'unrepeatdist', None]或者是ReproducibleSampler,RandomBatchSampler | |||
# dist_sampler 可以选择的有['dist', 'unrepeatdist', None]或者是ReproducibleSampler,ReproduceBatchSampler | |||
# reproducible 是 True 和 False | |||
# 需要 check 返回的 sampler 和 dataloader 都不同了 | |||
@@ -4,7 +4,7 @@ from fastNLP.core.drivers.torch_driver.utils import ( | |||
replace_batch_sampler, | |||
replace_sampler, | |||
) | |||
from fastNLP.core.samplers import RandomBatchSampler, RandomSampler | |||
from fastNLP.core.samplers import ReproduceBatchSampler, RandomSampler | |||
from torch.utils.data import DataLoader, BatchSampler | |||
from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
@@ -14,12 +14,12 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
def test_replace_batch_sampler(): | |||
dataset = TorchNormalDataset(10) | |||
dataloader = DataLoader(dataset, batch_size=32) | |||
batch_sampler = RandomBatchSampler(dataloader.batch_sampler, batch_size=16, drop_last=False) | |||
batch_sampler = ReproduceBatchSampler(dataloader.batch_sampler, batch_size=16, drop_last=False) | |||
replaced_loader = replace_batch_sampler(dataloader, batch_sampler) | |||
assert not (replaced_loader is dataloader) | |||
assert isinstance(replaced_loader.batch_sampler, RandomBatchSampler) | |||
assert isinstance(replaced_loader.batch_sampler, ReproduceBatchSampler) | |||
assert isinstance(replaced_loader.dataset, TorchNormalDataset) | |||
assert len(replaced_loader.dataset) == len(dataset) | |||
assert replaced_loader.batch_sampler.batch_size == 16 | |||
@@ -5,7 +5,7 @@ import pytest | |||
from itertools import chain | |||
from copy import deepcopy | |||
from fastNLP.core.samplers import RandomBatchSampler, BucketedBatchSampler | |||
from fastNLP.core.samplers import ReproduceBatchSampler, BucketedBatchSampler, RandomBatchSampler | |||
from fastNLP.core.drivers.torch_driver.utils import replace_batch_sampler | |||
from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
@@ -19,7 +19,7 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
# before_batch_size = 7 | |||
# dataset = TorchNormalDataset(num_of_data=100) | |||
# dataloader = DataLoader(dataset, batch_size=before_batch_size) | |||
# re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# re_batchsampler = ReproduceBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
# | |||
# forward_steps = 3 | |||
@@ -29,15 +29,15 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
# | |||
# # 1. 保存状态 | |||
# _get_re_batchsampler = dataloader.batch_sampler | |||
# assert isinstance(_get_re_batchsampler, RandomBatchSampler) | |||
# assert isinstance(_get_re_batchsampler, ReproduceBatchSampler) | |||
# state = _get_re_batchsampler.state_dict() | |||
# assert state == {"index_list": array("I", list(range(100))), "num_consumed_samples": forward_steps*before_batch_size, | |||
# "sampler_type": "RandomBatchSampler"} | |||
# "sampler_type": "ReproduceBatchSampler"} | |||
# | |||
# # 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 = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# re_batchsampler = ReproduceBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# re_batchsampler.load_state_dict(state) | |||
# dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
# | |||
@@ -54,7 +54,7 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
# # 改变 batch_size; | |||
# after_batch_size = 3 | |||
# dataloader = DataLoader(dataset, batch_size=after_batch_size) | |||
# re_batchsampler = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# re_batchsampler = ReproduceBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# re_batchsampler.load_state_dict(state) | |||
# dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
# | |||
@@ -100,7 +100,7 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
# 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 = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# re_batchsampler = ReproduceBatchSampler(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; | |||
@@ -112,13 +112,13 @@ from tests.helpers.datasets.torch_data import TorchNormalDataset | |||
# | |||
# # 1. 保存状态 | |||
# _get_re_batchsampler = dataloader.batch_sampler | |||
# assert isinstance(_get_re_batchsampler, RandomBatchSampler) | |||
# assert isinstance(_get_re_batchsampler, ReproduceBatchSampler) | |||
# 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 = RandomBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# re_batchsampler = ReproduceBatchSampler(dataloader.batch_sampler, dataloader.batch_size, drop_last=False) | |||
# re_batchsampler.load_state_dict(state) | |||
# dataloader = replace_batch_sampler(dataloader, re_batchsampler) | |||
# | |||
@@ -511,3 +511,313 @@ class TestBucketedBatchSampler: | |||
already_seen_set.update(batch) | |||
assert len(already_seen_set)==len(dataset) if drop_last is False else len(already_seen_set)<=len(dataset) | |||
class TestRandomBatchSampler: | |||
@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 = RandomBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size, | |||
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 = RandomBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size, | |||
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) | |||
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 = RandomBatchSampler(dataset, length=dataset.data, batch_size=before_batch_size, | |||
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: | |||
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 = RandomBatchSampler(dataset, length=dataset.data, batch_size=after_batch_size, | |||
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] | |||
for batch in re_batchsampler3: | |||
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 | |||
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 = RandomBatchSampler(dataset, length=dataset.data, batch_size=after_batch_size, | |||
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 | |||
for batch in re_batchsampler4: | |||
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 = RandomBatchSampler(dataset, length=dataset.data, batch_size = batch_size, | |||
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: | |||
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 = RandomBatchSampler(dataset, length=dataset.data, batch_size = batch_size, | |||
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: | |||
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 = RandomBatchSampler(dataset, length=dataset.data, batch_size=new_batch_size, | |||
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] | |||
for batch in new_sampler: | |||
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 = RandomBatchSampler(dataset, length=dataset.data, batch_size=new_batch_size, | |||
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] | |||
for batch in new_sampler: | |||
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_replicas', [2, 3]) | |||
def test_multi_same_bucket(self, shuffle, drop_last, pad, num_samples, num_replicas): | |||
# def test_multi_same_bucket(self, shuffle=True, drop_last=True, pad=True, num_samples=623, num_replicas=2): | |||
dataset = DatasetWithVaryLength(num_of_data=num_samples) | |||
batch_size = 6 | |||
if num_replicas*batch_size > num_samples: | |||
return | |||
num_batch_per_bucket = 10 | |||
samplers = [] | |||
lengths = [] | |||
for i in range(num_replicas): | |||
sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=batch_size, | |||
shuffle=shuffle, drop_last=drop_last) | |||
sampler.set_distributed(num_replicas, rank=i, pad=pad) | |||
sampler.set_epoch(0) | |||
samplers.append(sampler) | |||
lengths.append(len(list(iter(sampler)))) | |||
assert len(set(lengths))==1 | |||
@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_replicas', [1, 2, 3]) | |||
def test_multi_save_load(self, shuffle, drop_last, pad, num_samples, num_replicas): | |||
""" | |||
测试是否能够正确地恢复使用过的(forward)数据 | |||
:return: | |||
""" | |||
batch_size = 6 | |||
dataset = DatasetWithVaryLength(num_of_data=num_samples) | |||
samplers = [] | |||
num_consumed_samples_array = list(range(0, num_samples+num_replicas, num_replicas)) | |||
for i in range(num_replicas): | |||
sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=batch_size, | |||
shuffle=shuffle, drop_last=drop_last) | |||
sampler.set_distributed(num_replicas=num_replicas, rank=i, pad=pad) | |||
samplers.append(sampler) | |||
count = 0 | |||
already_seen_sets = [set()] | |||
already_seen_set = set() | |||
for batchs in zip(*samplers): | |||
batch = chain(*batchs) | |||
already_seen_set.update(batch) | |||
already_seen_sets.append(deepcopy(already_seen_set)) | |||
count += 1 | |||
if count > 3: | |||
break | |||
states = samplers[0].state_dict() | |||
for i in range(len(already_seen_sets)): | |||
states['num_consumed_samples'] = num_consumed_samples_array[i] | |||
sampler = BucketedBatchSampler(dataset, length=dataset.data, batch_size=batch_size+1, | |||
shuffle=shuffle, drop_last=drop_last) | |||
sampler.set_epoch(0) | |||
already_seen_set = deepcopy(already_seen_sets[i]) | |||
for batch in sampler: | |||
already_seen_set.update(batch) | |||
assert len(already_seen_set) == len(dataset) if drop_last is False else len(already_seen_set) <= len( | |||
dataset) | |||
# 测试保存之后再次保存 | |||
sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=batch_size + 1, | |||
shuffle=shuffle, | |||
drop_last=drop_last) | |||
sampler.set_epoch(0) | |||
states['num_consumed_samples'] = num_consumed_samples_array[2] | |||
if len(already_seen_sets)<3: | |||
return | |||
already_seen_set = already_seen_sets[2] | |||
count = 0 | |||
for batch in sampler: | |||
already_seen_set.update(batch) | |||
count += 1 | |||
if count > 6: | |||
break | |||
states = sampler.state_dict() | |||
num_consumed_samples_array = list(range(len(dataset))) | |||
states['num_consumed_samples'] = num_consumed_samples_array[count] | |||
sampler = RandomBatchSampler(dataset, length=dataset.data, batch_size=batch_size//2, | |||
shuffle=shuffle, | |||
drop_last=drop_last) | |||
sampler.load_state_dict(states) | |||
sampler.set_epoch(0) | |||
for batch in sampler: | |||
already_seen_set.update(batch) | |||
assert len(already_seen_set)==len(dataset) if drop_last is False else len(already_seen_set)<=len(dataset) |