Update distributed checkpoint recipes

recipes_source/distributed_async_checkpoint_recipe.rst

@@ -51,10 +51,9 @@ Specifically:
     import torch.multiprocessing as mp
     import torch.nn as nn
 
-    from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+    from torch.distributed.fsdp import fully_shard
     from torch.distributed.checkpoint.state_dict import get_state_dict, set_state_dict
     from torch.distributed.checkpoint.stateful import Stateful
-    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
 
     CHECKPOINT_DIR = "checkpoint"
 
@@ -74,7 +73,7 @@ Specifically:
 
         def state_dict(self):
             # this line automatically manages FSDP FQN's, as well as sets the default state dict type to FSDP.SHARDED_STATE_DICT
-            model_state_dict, optimizer_state_dict = get_state_dict(model, optimizer)
+            model_state_dict, optimizer_state_dict = get_state_dict(self.model, self.optimizer)
             return {
                 "model": model_state_dict,
                 "optim": optimizer_state_dict
@@ -105,7 +104,7 @@ Specifically:
         os.environ["MASTER_PORT"] = "12355 "
 
         # initialize the process group
-        dist.init_process_group("nccl", rank=rank, world_size=world_size)
+        dist.init_process_group("gloo", rank=rank, world_size=world_size)
         torch.cuda.set_device(rank)
 
 
@@ -119,7 +118,7 @@ Specifically:
 
         # create a model and move it to GPU with id rank
         model = ToyModel().to(rank)
-        model = FSDP(model)
+        model = fully_shard(model)
 
         loss_fn = nn.MSELoss()
         optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
@@ -158,9 +157,9 @@ Specifically, this optimization attacks the main overhead of asynchronous checkp
 checkpoint requests users can take advantage of direct memory access to speed up this copy.
 
 .. note::
-   The main drawback of this optimization is the persistence of the buffer in between checkpointing steps. Without 
-   the pinned memory optimization (as demonstrated above), any checkpointing buffers are released as soon as 
-   checkpointing is finished. With the pinned memory implementation, this buffer is maintained between steps, 
+   The main drawback of this optimization is the persistence of the buffer in between checkpointing steps. Without
+   the pinned memory optimization (as demonstrated above), any checkpointing buffers are released as soon as
+   checkpointing is finished. With the pinned memory implementation, this buffer is maintained between steps,
    leading to the same
    peak memory pressure being sustained through the application life.
 
@@ -175,11 +174,10 @@ checkpoint requests users can take advantage of direct memory access to speed up
     import torch.multiprocessing as mp
     import torch.nn as nn
 
-    from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+    from torch.distributed.fsdp import fully_shard
     from torch.distributed.checkpoint.state_dict import get_state_dict, set_state_dict
     from torch.distributed.checkpoint.stateful import Stateful
-    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
-    from torch.distributed.checkpoint import StorageWriter
+    from torch.distributed.checkpoint import FileSystemWriter as StorageWriter
 
     CHECKPOINT_DIR = "checkpoint"
 
@@ -199,7 +197,7 @@ checkpoint requests users can take advantage of direct memory access to speed up
 
         def state_dict(self):
             # this line automatically manages FSDP FQN's, as well as sets the default state dict type to FSDP.SHARDED_STATE_DICT
-            model_state_dict, optimizer_state_dict = get_state_dict(model, optimizer)
+            model_state_dict, optimizer_state_dict = get_state_dict(self.model, self.optimizer)
             return {
                 "model": model_state_dict,
                 "optim": optimizer_state_dict
@@ -230,7 +228,7 @@ checkpoint requests users can take advantage of direct memory access to speed up
         os.environ["MASTER_PORT"] = "12355 "
 
         # initialize the process group
-        dist.init_process_group("nccl", rank=rank, world_size=world_size)
+        dist.init_process_group("gloo", rank=rank, world_size=world_size)
         torch.cuda.set_device(rank)
 
 
@@ -244,7 +242,7 @@ checkpoint requests users can take advantage of direct memory access to speed up
 
         # create a model and move it to GPU with id rank
         model = ToyModel().to(rank)
-        model = FSDP(model)
+        model = fully_shard(model)
 
         loss_fn = nn.MSELoss()
         optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
@@ -254,7 +252,7 @@ checkpoint requests users can take advantage of direct memory access to speed up
         # into a persistent buffer with pinned memory enabled.
         # Note: It's important that the writer persists in between checkpointing requests, since it maintains the
         # pinned memory buffer.
-        writer = StorageWriter(cached_state_dict=True)
+        writer = StorageWriter(cache_staged_state_dict=True, path=CHECKPOINT_DIR)
         checkpoint_future = None
         for step in range(10):
             optimizer.zero_grad()

recipes_source/distributed_checkpoint_recipe.rst

@@ -59,10 +59,9 @@ Now, let's create a toy module, wrap it with FSDP, feed it with some dummy input
     import torch.multiprocessing as mp
     import torch.nn as nn
 
-    from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+    from torch.distributed.fsdp import fully_shard
     from torch.distributed.checkpoint.state_dict import get_state_dict, set_state_dict
     from torch.distributed.checkpoint.stateful import Stateful
-    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
 
     CHECKPOINT_DIR = "checkpoint"
 
@@ -127,7 +126,7 @@ Now, let's create a toy module, wrap it with FSDP, feed it with some dummy input
 
         # create a model and move it to GPU with id rank
         model = ToyModel().to(rank)
-        model = FSDP(model)
+        model = fully_shard(model)
 
         loss_fn = nn.MSELoss()
         optimizer = torch.optim.Adam(model.parameters(), lr=0.1)
@@ -152,7 +151,7 @@ Now, let's create a toy module, wrap it with FSDP, feed it with some dummy input
             join=True,
         )
 
-Please go ahead and check the `checkpoint` directory. You should see 8 checkpoint files as shown below.
+Please go ahead and check the `checkpoint` directory. You should see checkpoint files corresponding to the number of files as shown below. For example, if you have 8 devices, you should see 8 files.
 
 .. figure:: /_static/img/distributed/distributed_checkpoint_generated_files.png
    :width: 100%
@@ -183,7 +182,7 @@ The reason that we need the ``state_dict`` prior to loading is:
     import torch.multiprocessing as mp
     import torch.nn as nn
 
-    from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+    from torch.distributed.fsdp import fully_shard
 
     CHECKPOINT_DIR = "checkpoint"
 
@@ -248,7 +247,7 @@ The reason that we need the ``state_dict`` prior to loading is:
 
         # create a model and move it to GPU with id rank
         model = ToyModel().to(rank)
-        model = FSDP(model)
+        model = fully_shard(model)
 
         optimizer = torch.optim.Adam(model.parameters(), lr=0.1)