Adding documentation for Prefetcher (#835)

Summary: Pull Request resolved: #835

Test Plan: Imported from OSS

Reviewed By: ejguan

Differential Revision: D40474180

Pulled By: NivekT

fbshipit-source-id: 9b67d23c8bc1899d52654f54adecb42e17748f5e

docs/source/torchdata.datapipes.iter.rst

@@ -181,6 +181,7 @@ A miscellaneous set of DataPipes with different functionalities.
  LengthSetter
  MapToIterConverter
  OnDiskCacheHolder
+ Prefetcher
  RandomSplitter
  ShardingFilter
 

torchdata/datapipes/iter/util/prefetcher.py

@@ -29,6 +29,28 @@ def __init__(self, source_datapipe, buffer_size):
 
 @functional_datapipe("prefetch")
 class PrefetcherIterDataPipe(IterDataPipe):
+ """
+ Prefetches elements from the source DataPipe and puts them into a buffer (functional name: ``prefetch``).
+ Prefetching performs the operations (e.g. I/O, computations) of the DataPipes up to this one ahead of time
+ and stores the result in the buffer, ready to be consume by the subsequent DataPipe. It has no effect aside
+ from getting the sample ready ahead of time.
+
+ This is used by ``PrototypeMultiProcessingReadingService`` when the arguments
+ ``prefetch_worker`` (for prefetching at each worker process) or
+ ``prefetch_mainloop`` (for prefetching at the moain loop) are greater than 0.
+
+ Beyond the built-in use cases, this can be useful to put after I/O DataPipes that have
+ expensive I/O operations (e.g. takes a long time to request a file from a remote server).
+
+ Args:
+ source_datapipe: IterDataPipe from which samples are prefetched
+ buffer_size: the size of the buffer which stores the prefetched samples
+
+ Example:
+ >>> from torchdata.datapipes.iter import IterableWrapper
+ >>> dp = IterableWrapper(file_paths).open_files().prefetch(5)
+ """
+
  def __init__(self, source_datapipe, buffer_size: int = 10):
  self.source_datapipe = source_datapipe
  if buffer_size <= 0: