def __call__(self, cls):
if isinstance(cls, Type): # type: ignore
if not issubclass(cls, IterDataPipe):
raise TypeError(
'`functional_datapipe` can only decorate IterDataPipe')
# with non_deterministic decorator
else:
if not isinstance(cls, non_deterministic) and \
not (hasattr(cls, '__self__') and
isinstance(cls.__self__, non_deterministic)):
raise TypeError(
'`functional_datapipe` can only decorate IterDataPipe')
IterDataPipe.register_datapipe_as_function(self.name, cls)
return cls
def __getstate__(self):
if IterDataPipe.getstate_hook is not None:
return IterDataPipe.getstate_hook(self)
if DILL_AVAILABLE:
dill_function = dill.dumps(self.filter_fn)
else:
dill_function = self.filter_fn
state = (self.datapipe, dill_function, self.args, self.kwargs, self.drop_empty_batches)
return state
def __getstate__(self):
if IterDataPipe.getstate_hook is not None:
return IterDataPipe.getstate_hook(self)
state = (
self.main_datapipe,
self.num_instances,
self.buffer_size,
)
return state
def __call__(self, cls):
if issubclass(cls, IterDataPipe):
if isinstance(cls, Type): # type: ignore[arg-type]
if not isinstance(cls, _DataPipeMeta):
raise TypeError(
'`functional_datapipe` can only decorate IterDataPipe')
# with non_deterministic decorator
else:
if not isinstance(cls, non_deterministic) and \
not (hasattr(cls, '__self__') and
isinstance(cls.__self__, non_deterministic)):
raise TypeError(
'`functional_datapipe` can only decorate IterDataPipe')
IterDataPipe.register_datapipe_as_function(
self.name,
cls,
enable_df_api_tracing=self.enable_df_api_tracing)
elif issubclass(cls, MapDataPipe):
MapDataPipe.register_datapipe_as_function(self.name, cls)
return cls
def __getstate__(self):
if IterDataPipe.getstate_hook is not None:
return IterDataPipe.getstate_hook(self)
serialized_fn_with_method = serialize_fn(self.classifier_fn)
state = (
self.main_datapipe,
self.num_instances,
self.buffer_size,
serialized_fn_with_method,
self.drop_none,
)
return state
def __getstate__(self):
if IterDataPipe.getstate_hook is not None:
return IterDataPipe.getstate_hook(self)
if DILL_AVAILABLE:
dill_function = dill.dumps(self.fn)
else:
dill_function = self.fn
state = (
self.datapipe,
dill_function,
self.input_col,
self.output_col,
)
return state
def __getstate__(self):
if IterDataPipe.getstate_hook is not None:
return IterDataPipe.getstate_hook(self)
if DILL_AVAILABLE:
dill_function = dill.dumps(self.classifier_fn)
else:
dill_function = self.classifier_fn
state = (
self.main_datapipe,
self.num_instances,
self.buffer_size,
dill_function,
self.drop_none,
)
return state
def __init__(self,
datapipe: IterDataPipe,
batch_size: int,
drop_last: bool = False,
unbatch_level: int = 0,
) -> None:
assert batch_size > 0, "Batch size is required to be larger than 0!"
super().__init__()
if unbatch_level == 0:
self.datapipe = datapipe
else:
self.datapipe = datapipe.unbatch(unbatch_level=unbatch_level)
self.unbatch_level = unbatch_level
self.batch_size = batch_size
self.drop_last = drop_last
self.length = None
self.wrapper_class = DataChunk
def __getstate__(self):
if IterDataPipe.getstate_hook is not None:
return IterDataPipe.getstate_hook(self)
if DILL_AVAILABLE:
dill_function = dill.dumps(self.group_key_fn)
else:
dill_function = self.group_key_fn
state = (
self.datapipe,
dill_function,
self.buffer_size,
self.group_size,
self.guaranteed_group_size,
self.drop_remaining,
)
return state
def list_connected_datapipes(scan_obj, exclude_primitive):
f = io.BytesIO()
p = pickle.Pickler(
f
) # Not going to work for lambdas, but dill infinite loops on typing and can't be used as is
def stub_pickler(obj):
return stub_unpickler, ()
captured_connections = []
def getstate_hook(obj):
state = {}
for k, v in obj.__dict__.items():
if callable(v) or isinstance(v, PRIMITIVE):
continue
state[k] = v
return state
def reduce_hook(obj):
if obj == scan_obj:
raise NotImplementedError
else:
captured_connections.append(obj)
return stub_unpickler, ()
try:
IterDataPipe.set_reduce_ex_hook(reduce_hook)
if exclude_primitive:
IterDataPipe.set_getstate_hook(getstate_hook)
p.dump(scan_obj)
except AttributeError: # unpickable DataPipesGraph
pass # TODO(VitalyFedyunin): We need to tight this requirement after migrating from old DataLoader
finally:
IterDataPipe.set_reduce_ex_hook(None)
if exclude_primitive:
IterDataPipe.set_getstate_hook(None)
return captured_connections
def __call__(self, cls):
if not (isinstance(cls, non_deterministic) or issubclass(cls, IterDataPipe)):
raise Exception('Can only decorate IterDataPipe')
IterDataPipe.register_datapipe_as_function(self.name, cls)
return cls
