vllm-omni/docs/user_guide/diffusion/offloader/distributed_layerwise_offload.md

# Distributed Layerwise Offloading

Distributed layerwise offloading (DLO) extends block streaming to multi-device
deployments. With AllGather enabled, each rank stores roughly `1 / dp_size` of
the host weights and reconstructs each layer at runtime. Without AllGather,
each rank streams a complete block independently. Compatible TP1 deployments
can share checkpoint-backed host pages among processes on the same node;
otherwise DLO streams the ordinary loader's rank-local tensors.

See the [DLO feature design](../../../design/feature/offloader/distributed_layerwise_offload.md)
for the implementation contract and compatibility matrix.

## Execution model

DLO overlaps three operations with a fixed two-block device buffer:

```text
Compute stream:  [Layer N]          [Layer N+1]        [Layer N+2]
H2D stream:      [H2D shard N+1]    [H2D shard N+2]
AllGather:       [AG N+1]           [AG N+2]
Slots:           slot 0: Layer N    slot 1: Layer N+1
```

AllGather communicates only request-independent weight shards, so data-
parallel ranks may process different requests concurrently.

## Usage

```bash
# Four ranks with sharded host weights and AllGather
vllm serve /path/to/model --omni \
  --enable-distributed-layerwise-offload \
  --data-parallel-size 4

# Standard-loader rank-local weights, without DLO AllGather
vllm serve /path/to/model --omni \
  --enable-distributed-layerwise-offload \
  --data-parallel-size 4 \
  --dlo-no-use-allgather

# Sequence parallel deployment
vllm serve /path/to/model --omni \
  --enable-distributed-layerwise-offload \
  --usp 4
```

```python
from vllm_omni import Omni

omni = Omni(
    model="/path/to/model",
    enable_distributed_layerwise_offload=True,
    dlo_use_allgather=True,
)
```

## Flags

| Flag | Meaning | Default |
| --- | --- | --- |
| `--enable-distributed-layerwise-offload` | Enable DLO | `false` |
| `--data-parallel-size N` | DP ranks and AllGather weight-sharding group | `1` |
| `--dlo-use-allgather` | Shard host weights and reconstruct with AllGather | `true` |
| `--dlo-no-use-allgather` | Stream complete rank-local blocks without a DLO weight collective | `false` |
| `--dlo-resident-layers N` | Keep N leading main-DiT blocks on device; requires no-AllGather and model-declared resident paths | `0` |

## Host-weight loading

The diffusion loader chooses host storage before DLO is enabled. It first
attempts to build a complete, validated direct-checkpoint mmap plan. If names,
coverage, shape, dtype, topology, or loader-callback compatibility cannot be
proven, it runs the ordinary model loader instead. DLO consumes that result and
does not make a second checkpoint-compatibility decision.

With direct checkpoint mmap, the loader:

1. saves non-persistent buffers such as RoPE frequencies;
2. moves the normally created transformer to the meta device;
3. loads checkpoint tensors as mmap views backed by the shared OS page cache;
4. applies any loader-owned bounded layout adapters while packing blocks;
5. restores saved non-persistent buffers; and
6. preserves `post_load_weights()` and `validate_loaded_weights()` lifecycle
   hooks.

For AllGather with a group larger than one, each process copies only its
persistent shard and then releases the source mapping. For no-AllGather, each
process keeps the mapping open and packs complete blocks through two bounded
pinned staging slots. Processes mapping the same files on one node share the
immutable pages; no-AllGather still performs a complete-block H2D copy in each
process.

When the effective DLO group size is one, `dlo_use_allgather=True` does not
perform a collective and uses the same rank-local transfer behavior.

## Declarative topology

Models may declare an `OffloadPlan` instead of embedding offload logic:

```python
from vllm_omni.diffusion.offloader import OffloadPlan


class MyPipeline(nn.Module):
    _dit_modules = ["transformer"]
    _offload_plan = OffloadPlan(
        block_attrs={"transformer": ("blocks",)},
        offload_submodules={"context_encoder": "layers"},
    )
```

When no plan exists, discovery falls back to
`_layerwise_offload_blocks_attrs` and then heuristic attribute lookup.

## Data-parallel concurrency

With `data_parallel_size > 1` and AllGather enabled, the scheduler can process
up to `dp_size` requests per denoising step. Every concurrent request must set
the same explicit `num_inference_steps`; `None` is rejected because every rank
must enter each collective.

## Limitations

- Direct checkpoint mmap currently requires TP1. TP greater than one falls
  back before model mutation to the ordinary TP-aware loader; DLO can then
  stream that runtime layout. Broader TP combinations remain experimental.
- HSDP plus AllGather is rejected to avoid double sharding. HSDP without
  AllGather has limited end-to-end validation.
- Online quantization uses the ordinary loader with no-AllGather. It remains
  incompatible with DLO AllGather.
- Resident leading layers require `--dlo-no-use-allgather` and a model
  `OffloadPlan` that declares eligible `resident_dit_paths`.
- DP concurrency requires an explicit, identical inference-step count.

Sharing transformed TP or quantized runtime layouts through a normalized mmap
cache is a follow-up design in
[RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195), not part of
the direct-checkpoint path.

See the [Cosmos3 DistOffload recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/cosmos3/Cosmos3-DistOffload.md)
for an end-to-end example.