[chore] OSS: add a small sphinx tutorial, similar to README (#92)

Add a small tutorial, similar to the OSS README

README.md

@@ -11,7 +11,7 @@ fairscale supports:
 
 Run a 4-layer model on 2 GPUs. The first two layers run on cuda:0 and the next two layers run on cuda:1.
 
-```bash
+```python
 import torch
 
 import fairscale
@@ -23,7 +23,7 @@ model = fairscale.nn.Pipe(model, balance=[2, 2], devices=[0, 1], chunks=8)
 ### Optimizer state sharding (ZeRO)
 See a more complete example [here](https://github.com/facebookresearch/fairscale/blob/oss_async_broadcast/benchmarks/oss.py), but a minimal example could look like the following :
 
-```bash
+```python
 import torch
 from fairscale.optim.oss import OSS
 
@@ -58,7 +58,7 @@ def train(
             optimizer.step()
 
 if __name__ == "__main__":
-    # supposing that WORLD_SIZE and EPOCHS are somehow defined somewhere
+    # Supposing that WORLD_SIZE and EPOCHS are somehow defined somewhere
     mp.spawn(
         train,
         args=(

docs/source/tutorials/index.rst

@@ -5,3 +5,5 @@ Tutorials
    :maxdepth: 1
 
    pipe
+
+   oss

docs/source/tutorials/oss.rst

+Optimizer state sharding
+========================
+
+Using torch.nn.parallel.DistributedDataParallel leads to some wasted communications, but it is possible and makes OSS a drop in solution in your existing torch distributed code.
+Let's suppose that your trainer looks likemake html
+
+.. code-block:: default
+
+
+    import torch
+
+    def train(
+        rank: int,
+        world_size: int,
+        epochs: int):
+
+        # DDP
+        dist_init(rank, world_size)
+
+        # Problem statement
+        model = myAwesomeModel()
+        dataloader = mySuperFastDataloader()
+        loss = myVeryRelevantLoss()
+
+        base_optimizer_arguments = {} # any optimizer specific arguments, LR, momentum, etc...
+        optimizer = torch.optim.SGD(params=model.parameters(), **base_optimizer_arguments)
+
+        # Any relevant training loop, nothing specific to OSS. For example:
+        model.train()
+        for e in range(epochs):
+            for batch in dataloader:
+                # Train
+                model.zero_grad()
+                outputs = model(batch["inputs"])
+                loss = loss_fn(outputs, batch["label"])
+                torch.distributed.all_reduce(loss, op=torch.distributed.ReduceOp.SUM)
+                loss /= world_size
+                loss.backward()
+                optimizer.step()
+
+
+Then sharding the optimizer state is merely a matter of wrapping your optimizer in fairscale.optim.OSS, as follows
+
+.. code-block:: default
+
+
+    :emphasize-lines: 49, 65, 66
+    import torch
+    from fairscale.optim.oss import OSS
+
+    def train(
+        rank: int,
+        world_size: int,
+        epochs: int):
+
+        # DDP
+        dist_init(rank, world_size)
+
+        # Problem statement
+        model = myAwesomeModel()
+        dataloader = mySuperFastDataloader()
+        loss = myVeryRelevantLoss()
+
+        base_optimizer_arguments = {}  # pass any optimizer specific arguments here, or directly below when instantiating OSS
+        base_optimizer = torch.optim.SGD  # any pytorch compliant optimizer
+        optimizer = OSS(params=model.parameters(), optim=base_optimizer, **base_optimizer_arguments)
+
+        # Any relevant training loop, nothing specific to OSS. For example:
+        model.train()
+        for e in range(epochs):
+            for batch in dataloader:
+                # Train
+                model.zero_grad()
+                outputs = model(batch["inputs"])
+                loss = loss_fn(outputs, batch["label"])
+                torch.distributed.all_reduce(loss, op=torch.distributed.ReduceOp.SUM)
+                loss /= world_size
+                loss.backward()
+                optimizer.step()
+

fairscale/optim/oss.py

@@ -46,7 +46,9 @@ class OSS(Optimizer):
             torch.distributed group (default: group.WORLD)
     """
 
+    #: The optimizer used for a given shard
     optim: Optimizer
+
     in_super_constructor: bool
 
     def __init__(self, params: _params_t, optim: Type[Optimizer] = SGD, group: Any = dist.group.WORLD, **defaults: Any):