Attention

June 2024 Status Update: Removing DataPipes and DataLoader V2

We are re-focusing the torchdata repo to be an iterative enhancement of torch.utils.data.DataLoader. We do not plan on continuing development or maintaining the [DataPipes] and [DataLoaderV2] solutions, and they will be removed from the torchdata repo. We’ll also be revisiting the DataPipes references in pytorch/pytorch. In release torchdata==0.8.0 (July 2024) they will be marked as deprecated, and in 0.9.0 (Oct 2024) they will be deleted. Existing users are advised to pin to torchdata==0.8.0 or an older version until they are able to migrate away. Subsequent releases will not include DataPipes or DataLoaderV2. Please reach out if you suggestions or comments (please use this issue for feedback)

IterKeyZipper

class torchdata.datapipes.iter.IterKeyZipper(source_datapipe: IterDataPipe, ref_datapipe: IterDataPipe, key_fn: Callable, ref_key_fn: Optional[Callable] = None, keep_key: bool = False, buffer_size: int = 10000, merge_fn: Optional[Callable] = None)

Zips two IterDataPipes together based on the matching key (functional name: zip_with_iter). The keys are computed by key_fn and ref_key_fn for the two IterDataPipes, respectively. When there isn’t a match between the elements of the two IterDataPipes, the element from ref_datapipe is stored in a buffer. Then, the next element from ref_datapipe is tried. After a match is found, the merge_fn determines how they will be combined and returned (a tuple is generated by default).

Parameters:

• source_datapipe – IterKeyZipper will yield data based on the order of this IterDataPipe

• ref_datapipe – Reference IterDataPipe from which IterKeyZipper will find items with matching key for source_datapipe

• key_fn – Callable function that will compute keys using elements from source_datapipe

• ref_key_fn – Callable function that will compute keys using elements from ref_datapipe If it’s not specified, the key_fn will also be applied to elements from ref_datapipe

• keep_key – Option to yield the matching key along with the items in a tuple, resulting in (key, merge_fn(item1, item2)).

• buffer_size – The size of buffer used to hold key-data pairs from reference DataPipe until a match is found. If it’s specified as None, the buffer size is set as infinite.

• merge_fn – Function that combines the item from source_datapipe and the item from ref_datapipe, by default a tuple is created

Example

>>> from torchdata.datapipes.iter import IterableWrapper
>>> from operator import itemgetter
>>> def merge_fn(t1, t2):
>>>     return t1[1] + t2[1]
>>> dp1 = IterableWrapper([('a', 100), ('b', 200), ('c', 300)])
>>> dp2 = IterableWrapper([('a', 1), ('b', 2), ('c', 3), ('d', 4)])
>>> res_dp = dp1.zip_with_iter(dp2, key_fn=itemgetter(0),
>>>                            ref_key_fn=itemgetter(0), keep_key=True, merge_fn=merge_fn)
>>> list(res_dp)
[('a', 101), ('b', 202), ('c', 303)]