FSDP2 in TorchTitan

Why FSDP2?

FSDP2 is a rewrite of PyTorch's Fully Sharded Data Parallel (FSDP) API, removing the FlatParameter abstraction for better composability and simpler implementation.

Key improvements over FSDP1

DTensor-based sharding: Sharded parameters are DTensors on dim-0, enabling easy manipulation and communication-free sharded state dicts
Better memory management: Deterministic and lower GPU memory (7% reduction) by avoiding recordStream
Simplified API: Fewer arguments, no wrapper class

Performance

On Llama-7B with 8x H100s, FSDP2 achieves higher MFU with 7% lower peak memory than FSDP1, matching the same loss curve.

API Reference

python

from torch.distributed._composable.fsdp import fully_shard, MixedPrecisionPolicy, OffloadPolicy

@contract(state_cls=FSDPState)
def fully_shard(
    module: nn.Module,
    *,
    mesh: Optional[DeviceMesh] = None,
    reshard_after_forward: Union[bool, int] = True,
    mp_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
    offload_policy: OffloadPolicy = OffloadPolicy(),
) -> nn.Module:

Sharding Strategies (ZeRO Equivalents)

FSDP2 Configuration	FSDP1 Equivalent	DeepSpeed
1D mesh + `reshard_after_forward=True`	FULL_SHARD	ZeRO-3
1D mesh + `reshard_after_forward=False`	SHARD_GRAD_OP	ZeRO-2
2D mesh + `reshard_after_forward=True`	HYBRID_SHARD	MiCS
1D/2D mesh + `reshard_after_forward=8` (int)	-	ZeRO++ hpZ

Meta-Device Initialization

FSDP2 supports materializing tensors onto GPU after sharding:

python

# Initialize on meta device (no memory)
with torch.device("meta"):
    model = Transformer()

# Apply FSDP2 sharding
for module in model.modules():
    if isinstance(module, TransformerBlock):
        fully_shard(module)
fully_shard(model)

# Parameters still on meta device
for tensor in itertools.chain(model.parameters(), model.buffers()):
    assert tensor.device == torch.device("meta")

# Allocate sharded parameters on GPU
model.to_empty(device="cuda")

# Initialize weights
model.init_weights()

State Dict Differences

Operation	FSDP1	FSDP2
`model.state_dict()`	Full state dict	Sharded state dict (no communication)
`optim.state_dict()`	Local state dict	Sharded state dict (no communication)
`summon_full_params()`	Supported	Use `DTensor` APIs like `full_tensor()`
Gradient clipping	`FSDP.clip_grad_norm_()`	`nn.utils.clip_grad_norm_()`

Mixed Precision

python

from torch.distributed._composable.fsdp import MixedPrecisionPolicy

mp_policy = MixedPrecisionPolicy(
    param_dtype=torch.bfloat16,
    reduce_dtype=torch.float32,
    output_dtype=torch.bfloat16,
    cast_forward_inputs=True,
)

fully_shard(model, mp_policy=mp_policy)

HSDP (Hybrid Sharded Data Parallel)

For 2D parallelism with replication + sharding:

python

from torch.distributed.device_mesh import init_device_mesh

# Replicate across 4 groups, shard within 8 GPUs each
mesh = init_device_mesh("cuda", (4, 8), mesh_dim_names=("replicate", "shard"))

fully_shard(model, mesh=mesh)

Configuration in TorchTitan

toml

[parallelism]
# FSDP sharding degree (-1 = auto, use all available GPUs)
data_parallel_shard_degree = -1

# HSDP replication degree (1 = pure FSDP, >1 = HSDP)
data_parallel_replicate_degree = 1

Removed Arguments from FSDP1

These FSDP1 arguments are no longer needed:

auto_wrap_policy: Apply fully_shard directly to modules
backward_prefetch: Always uses BACKWARD_PRE
param_init_fn: Use meta-device initialization
device_id: Uses mesh's device automatically
sync_module_states: Not needed with DTensor
limit_all_gathers: New memory management doesn't need it
use_orig_params: Always true (no FlatParameter)