[DataPipe] Adding documentation for Prefetcher

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: