Update examples and tutorial after automatic sharding has landed

README.md

@@ -200,10 +200,11 @@ convert it to `MapDataPipe` as needed.
 
 Q: How is multiprocessing handled with DataPipes?
 
-A: Multi-process data loading is still handled by DataLoader, see the
+A: Multi-process data loading is still handled by the `DataLoader`, see the
 [DataLoader documentation for more details](https://pytorch.org/docs/stable/data.html#single-and-multi-process-data-loading).
-If you would like to shard data across processes, use `ShardingFilter` and provide a `worker_init_fn` as shown in the
-[tutorial](https://pytorch.org/data/beta/tutorial.html#working-with-dataloader).
+As of PyTorch version >= 1.12.0 (TorchData version >= 0.4.0), data sharding is automatically done for DataPipes within
+the `DataLoader` as long as a `ShardingFiler` DataPipe exists in your pipeline. Please see the
+[tutorial](https://pytorch.org/data/beta/tutorial.html#working-with-dataloader) for an example.
 
 Q: What is the upcoming plan for DataLoader?
 

docs/source/tutorial.rst

@@ -105,7 +105,7 @@ pass defined functions to DataPipes rather than lambda functions because the for
  return datapipe
 
 Lastly, we will put everything together in ``'__main__'`` and pass the DataPipe into the DataLoader. Note that
-if you choose to use `Batcher` while setting `batch_size > 1` for DataLoader, your samples will be
+if you choose to use ``Batcher`` while setting ``batch_size > 1`` for DataLoader, your samples will be
 batched more than once. You should choose one or the other.
 
 .. code:: python
@@ -154,21 +154,10 @@ In order for DataPipe sharding to work with ``DataLoader``, we need to add the f
  def build_datapipes(root_dir="."):
  datapipe = ...
  # Add the following line to `build_datapipes`
- # Note that it is somewhere after `Shuffler` in the DataPipe line
+ # Note that it is somewhere after `Shuffler` in the DataPipe line, but before expensive operations
  datapipe = datapipe.sharding_filter()
  return datapipe
 
- def worker_init_fn(worker_id):
- info = torch.utils.data.get_worker_info()
- num_workers = info.num_workers
- datapipe = info.dataset
- torch.utils.data.graph_settings.apply_sharding(datapipe, num_workers, worker_id)
-
- # Pass `worker_init_fn` into `DataLoader` within '__main__'
- ...
- dl = DataLoader(dataset=datapipe, shuffle=True, batch_size=5, num_workers=2, worker_init_fn=worker_init_fn)
- ...
-
 When we re-run, we will get:
 
 .. code::

examples/vision/imagefolder.py

@@ -9,8 +9,6 @@
 import re
 import threading
 
-import torch
-import torch.utils.data.backward_compatibility
 import torchvision.datasets as datasets
 import torchvision.datasets.folder
 import torchvision.transforms as transforms
@@ -168,7 +166,6 @@ def MyHTTPImageFolder(transform=None):
  batch_size=BATCH_SIZE,
  shuffle=False,
  num_workers=NUM_WORKERS,
- worker_init_fn=torch.utils.data.backward_compatibility.worker_init_fn,
  )
  items = list(dl)
  assert len(items) == 6
@@ -186,7 +183,6 @@ def MyHTTPImageFolder(transform=None):
  batch_size=BATCH_SIZE,
  shuffle=False,
  num_workers=NUM_WORKERS,
- worker_init_fn=torch.utils.data.backward_compatibility.worker_init_fn,
  )
 
  try:

test/test_local_io.py

@@ -77,13 +77,6 @@ def filepath_fn(temp_dir_name, name: str) -> str:
  return os.path.join(temp_dir_name, os.path.basename(name))
 
 
-def init_fn(worker_id):
- info = torch.utils.data.get_worker_info()
- num_workers = info.num_workers
- datapipe = info.dataset
- torch.utils.data.graph_settings.apply_sharding(datapipe, num_workers, worker_id)
-
-
 def _unbatch(x):
  return x[0]
 
@@ -753,7 +746,7 @@ def test_io_path_saver_file_lock(self):
 
  num_workers = 2
  line_lengths = []
- dl = DataLoader(saver_dp, num_workers=num_workers, worker_init_fn=init_fn, multiprocessing_context="spawn")
+ dl = DataLoader(saver_dp, num_workers=num_workers, multiprocessing_context="spawn")
  for filename in dl:
  with open(filename[0]) as f:
  lines = f.readlines()