add shard_predicates to fsdp2

tests/utils/test_prepare_module.py

@@ -274,6 +274,32 @@ def test_check_and_convert_mp_policy_dtypes(self) -> None:
         ):
             _check_and_convert_mp_policy_dtypes(invalid_mp_policy)
 
+    @patch("torchtnt.utils.prepare_module.fully_shard")
+    def test_fsdp2_strategy_shard_predicates(self, mock_fully_shard: Mock) -> None:
+        """
+        Ensure modules_to_shard and shard_predicates are applied sequentially
+        """
+
+        class SimpleModule(torch.nn.Module):
+            def __init__(self):
+                super(SimpleModule, self).__init__()
+                self.linear1 = torch.nn.Linear(10, 10, device="meta")
+                self.conv = torch.nn.Conv2d(10, 10, kernel_size=3, device="meta")
+
+        module = SimpleModule()
+        strategy = FSDP2Strategy(
+            modules_to_shard=[torch.nn.Conv2d],
+            shard_predicates=[lambda n, _: "linear" in n],
+        )
+        mock_mesh = MagicMock(spec=DeviceMesh)
+        mock_global_mesh = MagicMock(spec=GlobalMeshCoordinator)
+        mock_global_mesh.dp_mesh = mock_mesh
+        module = prepare_fsdp2(
+            module, torch.device("cpu"), strategy, global_mesh=mock_global_mesh
+        )
+        # shards self.linear, self.conv, and self
+        self.assertEqual(mock_fully_shard.call_count, 3)
+
     @patch("torchtnt.utils.prepare_module.fully_shard")
     def test_fsdp2_mesh(self, mock_fully_shard: Mock) -> None:
         """
@@ -285,7 +311,7 @@ def test_fsdp2_mesh(self, mock_fully_shard: Mock) -> None:
         mock_global_mesh = MagicMock(spec=GlobalMeshCoordinator)
         mock_global_mesh.dp_mesh = mock_mesh
 
-        strategy = FSDP2Strategy()
+        strategy = FSDP2Strategy(modules_to_shard=[torch.nn.Linear])
         module = prepare_fsdp2(
             module,
             torch.device("cpu"),

torchtnt/utils/prepare_module.py

@@ -17,6 +17,7 @@
     ContextManager,
     Dict,
     Iterable,
+    List,
     Literal,
     Optional,
     Set,
@@ -27,6 +28,7 @@
 
 import torch
 import torch.distributed as dist
+from pyre_extensions import none_throws
 from torch.distributed import ProcessGroup
 
 from torch.distributed._composable_state import _get_module_state
@@ -192,15 +194,18 @@ class FSDP2Strategy(Strategy):
     For more details on the args, see the link.
 
     Args:
-        modules_to_shard: A list of modules that should be sharded across devices. Options are 'all' to shard all submodules, or a list of module names/module types.
-        reshard_after_forward: If True, reshards parameters after the forward pass to optimize memory usage.
+        modules_to_shard: A list of modules that should be sharded across devices. Options are 'all' to shard all submodules, or a list of module names/module types. Specify None to not shard any modules with this flag.
+        shard_predicates: A list of predicates to decide which modules to shard with FSDP. Each predicate takes a module name (fqn) and the module itself. If any predicate returns True, the submodule is sharded.
+        reshard_after_forward: If True, reshards parameters post-forward pass to save memory.
         mp_policy: Controls mixed precision policy. If only dtype is provided, it will be used to cast all relevant parts of model. If None, no mixed precision is used
         cpu_offload: If True, enables CPU offloading of model parameters to reduce GPU memory usage.
 
     Note:
         It is recommended to specify specific modules to shard to avoid unnecessary sharding of all submodules, which has
         communication overhead.
 
+    Note: modules_to_shard and shard_predicates are applied sequentially. If a module is specified in modules_to_shard, it will be sharded regardless of shard_predicates, and vice-versa
+
     Example:
         >>> model
             TransformerDecoder(
@@ -222,10 +227,15 @@ class FSDP2Strategy(Strategy):
         >>> strategy = FSDP2Strategy(modules_to_shard=["TransformerSelfAttentionLayer", "Linear"])
     """
 
-    modules_to_shard: Union[
-        Literal["all"],
-        Iterable[Union[str, Type[torch.nn.Module]]],
-    ] = "all"
+    modules_to_shard: Optional[
+        Union[
+            Literal["all"],
+            Iterable[Union[str, Type[torch.nn.Module]]],
+        ]
+    ] = None
+    shard_predicates: List[Callable[[str, torch.nn.Module], bool]] = field(
+        default_factory=list
+    )
     reshard_after_forward: Union[bool, int] = True
     mp_policy: Optional[Union[str, torch.dtype, MixedPrecisionPolicy]] = None
     cpu_offload: bool = False
@@ -435,20 +445,20 @@ def prepare_fsdp2(
     shard_all = modules_to_shard == "all"
     shard_module_names: Set[str] = set()
     shard_module_types: Tuple[Type[torch.nn.Module], ...] = ()
-    if not shard_all:
+    if not shard_all and modules_to_shard is not None:
         assert (
             type(modules_to_shard) is not str
         ), f"modules_to_shard must be an iterable of modules or 'all', got {shard_all}"
 
-        for item in modules_to_shard:
+        for item in none_throws(modules_to_shard):
             if isinstance(item, str):
                 shard_module_names.add(item)
             else:
                 shard_module_types = shard_module_types + (item,)
 
     # apply the fsdp2 sharding bottoms up
     num_layers_sharded = 0
-    for _, m in reversed(list(module.named_modules())):
+    for n, m in reversed(list(module.named_modules())):
         if shard_all:
             # fully_shard does not support containers that do not implement forward
             if not isinstance(m, (torch.nn.ModuleList, torch.nn.ModuleDict)):
@@ -460,6 +470,13 @@ def prepare_fsdp2(
             # if m exists in shard_module_types, then shard it
             fully_shard(m, **fsdp_kwargs)
             num_layers_sharded += 1
+        elif len(strategy.shard_predicates) > 0:
+            # if shard_predicates is not empty, then check if any of the conditions are true
+            for predicate in strategy.shard_predicates:
+                if predicate(n, m):
+                    fully_shard(m, **fsdp_kwargs)
+                    num_layers_sharded += 1
+                    break
 
     if num_layers_sharded == 0:
         raise ValueError(