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. The optional
runtime-weight cache extends node-local sharing to final ordinary-loader
layouts, including matching TP coordinates across independent engines. On
CUDA, an explicit registration budget can make those shared mappings directly
H2D-copyable and avoid recurrent mmap-to-pinned host packing.

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 request-independent weight shards, but every member of
the transfer group must request the same next block in the same collective
order. A synchronized DP wave may contain different prompts only when those
requests follow the same denoising and block-execution path.

## 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

# Independently scheduled replicas sharing final runtime weights on one node
vllm serve /path/to/model --omni \
  --enable-distributed-layerwise-offload \
  --tensor-parallel-size 2 \
  --dlo-no-use-allgather \
  --dlo-enable-runtime-cache \
  --dlo-runtime-cache-dir /var/cache/vllm-omni/dlo-runtime-weights \
  --dlo-runtime-cache-pin-limit-gib 80

# 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=False,
    dlo_enable_runtime_cache=True,
    dlo_runtime_cache_dir="/var/cache/vllm-omni/dlo-runtime-weights",
    dlo_runtime_cache_pin_limit_gib=80,
)
```

## 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-enable-runtime-cache` | Normalize final ordinary-loader DiT weights into a node-local mmap cache; requires no-AllGather | `false` |
| `--dlo-runtime-cache-dir PATH` | Shared local-disk cache root | `~/.cache/vllm-omni/dlo-runtime-weights` |
| `--dlo-runtime-cache-lock-timeout SECONDS` | Maximum wait for another process publishing the same layout | `600` |
| `--dlo-runtime-cache-pin-limit-gib GIB` | Per-worker limit for registering the complete read-only runtime-cache mapping for direct H2D; zero keeps bounded host staging | `0` |
| `--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.

The shared-mmap optimization in this phase is supported only with TP1. TP
greater than one falls back before model mutation to ordinary TP-aware loading.
DLO may still consume those TP-local tensors, but this is a compatibility path:
it does not share checkpoint-backed runtime weights across DP replicas and
provides no shared-mmap host-memory guarantee.

When `--dlo-enable-runtime-cache` is set with no-AllGather, ordinary loading
gains a second storage opportunity. Every rank completes loader callbacks,
post-load processing, validation, and calibration. It then hashes the final
CPU DiT parameters and persistent buffers. One process per equivalent runtime
layout publishes sharded safetensors plus a checksummed manifest; peers
validate and mmap the same files. DLO replaces only tensor storage, preserves
the final `Parameter` objects, and releases the private allocator-backed copies
before installing its streaming hooks.

A positive `--dlo-runtime-cache-pin-limit-gib` deliberately selects this final
runtime layout even when a direct TP1 checkpoint plan is available. Raw
checkpoint mappings may require deferred loader transforms such as grouped-QKV
reordering; registering them would still leave recurrent host work. Publishing
the fully transformed layout once makes every streamed source directly
copyable.

The runtime layout identity excludes DP rank and SP rank. It includes TP world
size/rank and conservative SP implementation/world-size guards. Therefore two
independent `TP=2` engines normally create two entries: both TP0 workers map
one entry and both TP1 workers map the other. Cache sharing does not create a
process group or an inference-time collective.

With a zero registration budget, cached tensors remain pageable mmap views.
DLO packs each streamed block into one of two bounded pinned host slots before
the asynchronous H2D copy. This lowers steady-state PSS but adds recurrent
host-to-host copies; competing TP ranks or engines can turn DDR contention and
staging skew into collective wait time.

With a positive budget on CUDA, DLO instead attempts to register the complete
page-aligned cache mapping as read-only pinned host memory. Registration is
all-or-nothing: the total mapped range must fit the per-worker budget, and any
CUDA registration or verification failure rolls back partial registrations and
uses the bounded staging path. If CUDA cannot roll back a partial registration,
worker initialization aborts instead of unmapping memory that CUDA may still
own. On success, each immutable tensor view is copied directly into the
existing rotating device buffers on the H2D stream, so no private host staging
slots are allocated.

CUDA registration is process-local, but it does not clone file contents. The
same physical page-cache pages remain shared among equivalent workers; each
worker does, however, hold its own registration reference and must satisfy the
host's page-locking limits. Shutdown synchronizes outstanding GPU work,
unregisters every range, and only then closes the mmap handles.

The mmap plan skips only dedicated DiT weight sources. Other component sources,
such as a text encoder loaded through the shared diffusion loader, continue to
use their ordinary component loader. A checkpoint source that mixes DiT and
non-DiT weights falls back completely rather than leaving an unplanned
component uninitialized.

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.

## Runtime-cache lifecycle

Runtime-cache publication uses a per-layout POSIX file lock, a temporary
directory on the same filesystem, file and final-tensor SHA-256 validation,
`fsync`, and an atomic directory rename. A crashed writer releases its kernel
lock; the next writer removes only that layout's stale temporary directories
while holding the lock. Lock timeout, unsupported layout, insufficient disk,
or validation failure keeps the already-valid ordinary tensors.

The cache root is deliberately independent of `VLLM_CACHE_ROOT`, because
vLLM-Omni may isolate that variable between stage replicas. All consumers that
should share must receive the same `--dlo-runtime-cache-dir` on a local,
disk-backed filesystem. Do not place the cache on tmpfs when the goal is lower
host-memory PSS, and do not use a cross-node directory in this version.

There is no automatic eviction. Budget roughly one final DiT copy per distinct
TP coordinate, dtype/layout, and model version, plus temporary space while an
entry is published. Operators may remove entries or the complete cache root
only after all workers using its mmap files have stopped. The feature is
opt-in for this reason.

For direct H2D, set the registration limit above the complete page-aligned
layout size reported in the worker log. Partial registration is intentionally
unsupported because mixing direct and staged tensors would complicate buffer
lifetime and make performance topology-dependent. Registration is CUDA-only;
an insufficient budget, unsupported runtime, OS/CUDA pinning limit, or
registration error is reported and falls back to bounded staging.

## 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 to the ordinary TP-aware loader. With the optional runtime cache,
  equivalent processes at the same TP coordinate can then share that final
  rank-local layout in no-AllGather mode. A positive registration budget also
  chooses the final runtime cache at TP1 so deferred checkpoint transforms are
  not repeated during inference.
- HSDP plus AllGather is rejected to avoid double sharding. HSDP without
  AllGather has limited end-to-end validation.
- Per-tensor online FP8 linears use the ordinary loader and can run with either
  DLO transfer path. With AllGather, every rank temporarily materializes the
  complete FP8 model in host memory before DLO retains only its shard. Other
  online quantization methods require no-AllGather until their runtime layouts
  are validated.
- Runtime-cache v1 rejects all quantized, non-contiguous, aliased/tied,
  device-only, HSDP, expert-parallel, CFG-parallel, and PP layouts.
- 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.

The runtime cache improves steady-state host PSS, not startup peak: every rank
loads private weights before remapping, and validation adds full content-hash
passes. Direct H2D removes recurrent staging but does not change that startup
contract. Skip-load-on-cache-hit and cache eviction remain follow-up work in
[RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195).

Zero-budget staging is also not latency-neutral. In the four-L20X MiniMax-H3
smoke documented in the L20X runtime-cache matrix in the
[feature design](../../../design/feature/offloader/distributed_layerwise_offload.md#l20x-runtime-cache-smoke-matrix),
two independent TP2 engines reduced request-time host PSS by 36.6% with the
cache, but recurrent mmap-to-pinned staging reduced combined throughput by
69.3%. The registered path recovered near-private per-engine latency while
retaining the shared-memory benefit; a matched TP1 run improved mean wave
latency from 33.87 seconds to 4.88 seconds with byte-identical outputs. See the
feature design for the full smoke-test caveats and metrics.

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.