add new torchdata.nodes doc file (#1390) (#1392)

* add new torchdata.nodes doc file

* update main readme.md, move content of nodes readme.md to torchdata.nodes.rst

* main readme

* udpate readme

Author: divyanshk

README.md

@@ -35,6 +35,13 @@ provides state_dict and load_state_dict functionality. See
 examples
 [in this Colab notebook](https://colab.research.google.com/drive/1tonoovEd7Tsi8EW8ZHXf0v3yHJGwZP8M?usp=sharing).
 
+## torchdata.nodes
+
+torchdata.nodes is a library of composable iterators (not iterables!) that let you chain together common dataloading and
+pre-proc operations. It follows a streaming programming model, although "sampler + Map-style" can still be configured if
+you desire. See [torchdata.nodes main page](torchdata/nodes) for more details. Stay tuned for tutorial on
+torchdata.nodes coming soon!
+
 ## Installation
 
 ### Version Compatibility

docs/source/index.rst

@@ -37,6 +37,7 @@ Features described in this documentation are classified by release status:
    :caption: API Reference:
 
    torchdata.stateful_dataloader.rst
+   torchdata.nodes.rst
 
 
 .. toctree::

docs/source/torchdata.nodes.rst

@@ -0,0 +1,202 @@
+torchdata.nodes
+===============
+
+What is ``torchdata.nodes``?
+----------------------------
+
+``torchdata.nodes`` is a library of composable iterators (not
+iterables!) that let you chain together common dataloading and pre-proc
+operations. It follows a streaming programming model, although “sampler
++ Map-style” can still be configured if you desire.
+
+``torchdata.nodes`` adds more flexibility to the standard
+``torch.utils.data`` offering, and introduces multi-threaded parallelism
+in addition to multi-process (the only supported approach in
+``torch.utils.data.DataLoader``), as well as first-class support for
+mid-epoch checkpointing through a ``state_dict/load_state_dict``
+interface.
+
+``torchdata.nodes`` strives to include as many useful operators as
+possible, however it’s designed to be extensible. New nodes are required
+to subclass ``torchdata.nodes.BaseNode``, (which itself subclasses
+``typing.Iterator``) and implement ``next()``, ``reset(initial_state)``
+and ``get_state()`` operations (notably, not ``__next__``,
+``load_state_dict``, nor ``state_dict``)
+
+Getting started
+---------------
+
+Install torchdata with pip.
+
+.. code:: bash
+
+   pip install torchdata>=0.10.0
+
+Generator Example
+~~~~~~~~~~~~~~~~~
+
+Wrap a generator (or any iterable) to convert it to a BaseNode and get
+started
+
+.. code:: python
+
+   from torchdata.nodes import IterableWrapper, ParallelMapper, Loader
+
+   node = IterableWrapper(range(10))
+   node = ParallelMapper(node, map_fn=lambda x: x**2, num_workers=3, method="thread")
+   loader = Loader(node)
+   result = list(loader)
+   print(result)
+   # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
+
+Sampler Example
+~~~~~~~~~~~~~~~
+
+Samplers are still supported, and you can use your existing
+``torch.utils.data.Dataset``\ s
+
+.. code:: python
+
+   import torch.utils.data
+   from torchdata.nodes import SamplerWrapper, ParallelMapper, Loader
+
+
+   class SquaredDataset(torch.utils.data.Dataset):
+       def __getitem__(self, i: int) -> int:
+           return i**2
+       def __len__(self):
+           return 10
+
+   dataset = SquaredDataset()
+   sampler = RandomSampler(dataset)
+
+   # For fine-grained control of iteration order, define your own sampler
+   node = SamplerWrapper(sampler)
+   # Simply apply dataset's __getitem__ as a map function to the indices generated from sampler
+   node = ParallelMapper(node, map_fn=dataset.__getitem__, num_workers=3, method="thread")
+   # Loader is used to convert a node (iterator) into an Iterable that may be reused for multi epochs
+   loader = Loader(node)
+   print(list(loader))
+   # [25, 36, 9, 49, 0, 81, 4, 16, 64, 1]
+   print(list(loader))
+   # [0, 4, 1, 64, 49, 25, 9, 16, 81, 36]
+
+What’s the point of ``torchdata.nodes``?
+----------------------------------------
+
+We get it, ``torch.utils.data`` just works for many many use cases.
+However it definitely has a bunch of rough spots:
+
+Multiprocessing sucks
+~~~~~~~~~~~~~~~~~~~~~
+
+-  You need to duplicate memory stored in your Dataset (because of
+   Python copy-on-read)
+-  IPC is slow over multiprocess queues and can introduce slow startup
+   times
+-  You’re forced to perform batching on the workers instead of
+   main-process to reduce IPC overhead, increasing peak memory.
+-  With GIL-releasing functions and Free-Threaded Python,
+   multi-threading may not be GIL-bound like it used to be.
+
+``torchdata.nodes`` enables both multi-threading and multi-processing so
+you can choose what works best for your particular set up. Parallelism
+is primarily configured in Mapper operators giving you flexibility in
+the what, when, and how to parallelize.
+
+Map-style and random-access doesn’t scale
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Current map dataset approach is great for datasets that fit in memory,
+but true random-access is not going to be very performant once your
+dataset grows beyond memory limitations unless you jump through some
+hoops with a special sampler.
+
+``torchdata.nodes`` follows a streaming data model, where operators are
+Iterators that can be combined together to define a dataloading and
+pre-proc pipeline. Samplers are still supported (see example above) and
+can be combined with a Mapper to produce an Iterator
+
+Multi-Datasets do not fit well with the current implementation in ``torch.utils.data``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The current Sampler (one per dataloader) concepts start to break down
+when you start trying to combine multiple datasets. (For single
+datasets, they’re a great abstraction and will continue to be
+supported!)
+
+-  For multi-datasets, consider this scenario: ``len(dsA): 10``
+   ``len(dsB): 20``. Now we want to do round-robin (or sample uniformly)
+   between these two datasets to feed to our trainer. With just a single
+   sampler, how can you implement that strategy? Maybe a sampler that
+   emits tuples? What if you want to swap with RandomSampler, or
+   DistributedSampler? How will ``sampler.set_epoch`` work?
+
+``torchdata.nodes`` helps to address and scale multi-dataset dataloading
+by only dealing with Iterators, thereby forcing samplers and datasets
+together, focusing on composing smaller primitives nodes into a more
+complex dataloading pipeline.
+
+IterableDataset + multiprocessing requires additional dataset sharding
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Dataset sharding is required for data-parallel training, which is fairly
+reasonable. But what about sharding between dataloader workers? With
+Map-style datasets, distribution of work between workers is handled by
+the main process, which distributes sampler indices to workers. With
+IterableDatasets, each worker needs to figure out (through
+``torch.utils.data.get_worker_info``) what data it should be returning.
+
+Design choices
+--------------
+
+No Generator BaseNodes
+~~~~~~~~~~~~~~~~~~~~~~
+
+See https://github.com/pytorch/data/pull/1362 for more thoughts.
+
+One difficult choice we made was to disallow Generators when defining a
+new BaseNode implementation. However we dropped it and moved to an
+Iterator-only foundation for a few reasons around state management:
+
+1. We require explicit state handling in BaseNode implementations.
+   Generators store state implicitly on the stack and we found that we
+   needed to jump through hoops and write very convoluted code to get
+   basic state working with Generators
+2. End-of-iteration state dict: Iterables may feel more natural, however
+   a bunch of issues come up around state management. Consider the
+   end-of-iteration state dict. If you load this state_dict into your
+   iterable, should this represent the end-of-iteration or the start of
+   the next iteration?
+3. Loading state: If you call load_state_dict() on an iterable, most
+   users would expect the next iterator requested from it to start with
+   the loaded state. However what if iter is called twice before
+   iteration begins?
+4. Multiple Live Iterator problem: if you have one instance of an
+   Iterable, but two live iterators, what does it mean to call
+   state_dict() on the Iterable? In dataloading, this is very rare,
+   however we still need to work around it and make a bunch of
+   assumptions. Forcing devs that are implementing BaseNodes to reason
+   about these scenarios is, in our opinion, worse than disallowing
+   generators and Iterables.
+
+``torchdata.nodes.BaseNode`` implementations are Iterators. Iterators
+define ``next()``, ``get_state()``, and ``reset(initial_state | None)``.
+All re-initialization should be done in reset(), including initializing
+with a particular state if one is passed.
+
+However, end-users are used to dealing with Iterables, for example,
+
+::
+
+   for epoch in range(5):
+     # Most frameworks and users don't expect to call loader.reset()
+     for batch in loader:
+       ...
+     sd = loader.state_dict()
+     # Loading sd should not throw StopIteration right away, but instead start at the next epoch
+
+To handle this we keep all of the assumptions and special end-of-epoch
+handling in a single ``Loader`` class which takes any BaseNode and makes
+it an Iterable, handling the reset() calls and end-of-epoch state_dict
+loading.