Add normalized online FP8 cache for DLO

Signed-off-by: hsliuustc0106 <hsliuustc0106@users.noreply.github.com>

docs/design/feature/offloader/distributed_layerwise_offload.md

@@ -43,291 +43,21 @@ DLO weight collective.
 
 Host storage is selected separately from the transfer protocol. The loader can
 produce a direct-checkpoint mmap plan for a proven-compatible runtime layout;
-otherwise it uses the ordinary loader. In no-AllGather mode, the Phase B host
+otherwise it uses the ordinary loader. Consequently, no-AllGather replicas on
-weight cache can explicitly normalize supported final ordinary-loader DiT
+the same node can share immutable checkpoint pages when direct mmap is
-tensors into an immutable node-local mmap entry. Ordinary pinned weights remain
+selected, while the ordinary-loader fallback still keeps a private runtime
-the default after checkpoint mmap fallback when the cache is not selected; cache
+copy per process.
-publication and validation add startup and disk lifecycle costs. Replicas can
-share either proven-compatible checkpoint pages or, when selected, equivalent
-final runtime layouts. Under the existing
-pinned-memory policy, the backend attempts to make the complete final mapping
-directly H2D-copyable when the platform provides registration, avoiding the
-recurrent host packing otherwise required by mmap. An optional positive limit
-adds a per-worker safety ceiling. CUDA is the first implementation.
 
 The Phase A shared-mmap support boundary is TP1. TP greater than one is an
 ordinary-loader compatibility path: DLO can consume the resulting TP-local
-tensors. Direct checkpoint mmap still does not cover TP, but the Phase B cache
+tensors, but those configurations do not use checkpoint mmap and must not be
-can share each final TP coordinate among equivalent processes.
+used to claim shared-mmap host-memory savings.
 
-The compatibility matrix below defines the current implementation boundary.
+The compatibility matrix below describes the current implementation. The
-Hardware and performance qualification is tracked separately from this
+unit-level guards are covered, but not every parallelism combination has a
-long-lived design contract.
+full model-and-hardware end-to-end test.
 
-## Architecture
+## Design
-
-DLO is divided into four planes: configuration, loader-owned host storage,
-backend setup, and the per-block inference hot path. Keeping these decisions
-separate is intentional. In particular, an mmap cache is a host-storage choice;
-it does not imply a DLO AllGather, and disabling DLO AllGather does not decide
-whether the host source is private memory, checkpoint mmap, or host weight cache
-mmap. The shared control plane is described once below; the AllGather and
-no-AllGather startup and hot paths are then shown separately.
-
-### Component ownership
-
-The map includes optional mode-specific components, but the ownership rules
-and loader-to-backend boundary are shared.
-
-```mermaid
-flowchart TB
-    CLI["CLI / EngineArgs"] --> Config["OmniDiffusionConfig<br/>validation and operator policy"]
-    Config --> Runner["DiffusionModelRunner<br/>startup coordinator"]
-
-    subgraph LoaderPlane["Loader-owned host-storage plane"]
-        Loader["DiffusersPipelineLoader"]
-        Checkpoint["checkpoint mmap preflight"]
-        Ordinary["ordinary load + callbacks + calibration"]
-        HostWeightCache["host_weight_cache<br/>final-layout publication / join"]
-        Contract["HostWeightPlan<br/>backing + bindings + transforms"]
-        Loader --> Checkpoint
-        Loader -->|"registered final layout requested"| Ordinary
-        Checkpoint -->|"compatible"| Contract
-        Checkpoint -->|"fallback"| Ordinary
-        Ordinary -->|"host weight cache enabled"| HostWeightCache
-        HostWeightCache -->|"enabled and compatible"| Contract
-    end
-
-    Runner -->|"load_model"| Loader
-    Loader -->|"pipeline + optional HostWeightPlan"| Runner
-
-    Config --> Factory["get_offload_backend<br/>OffloadConfig normalization"]
-    Runner -->|"device + optional plan"| Factory
-    Factory --> Backend["DistributedLayerwiseOffloadBackend<br/>resource and transfer owner"]
-    Topology["existing DP / SP process groups"] --> Backend
-    Discovery["ModuleDiscovery + optional OffloadPlan"] --> Backend
-    Contract -.->|"passed once by the runner"| Backend
-
-    Backend -.->|"host weight cache + pinned-memory policy"| Registration["host_registration<br/>platform-neutral lifecycle"]
-    Registration --> CUDA["CUDA registration backend"]
-    Backend --> Hooks["DistributedLayerwiseOffloadHook<br/>one circular hook per streamed block"]
-    Forward["pipeline.forward"] --> Hooks
-    Hooks --> DataPath["two device slots<br/>H2D only or H2D + AllGather"]
-
-    Shutdown["DiffusionWorker.shutdown"] -->|"disable before process-group teardown"| Backend
-```
-
-| Component | Owns | Must not own |
-| --- | --- | --- |
-| CLI, engine arguments, and `OmniDiffusionConfig` | User policy, defaults, validation, and propagation to every worker | Checkpoint inspection, cache publication, or buffer allocation |
-| `OffloadConfig.from_od_config()` | Offload-strategy selection, effective DLO group size, AllGather policy, resident-layer count, pinned-memory policy, and optional registration ceiling | Host-weight compatibility decisions |
-| `DiffusersPipelineLoader` | Ordinary loading lifecycle and the decision to return an optional `HostWeightPlan` | DLO process groups or block-stream scheduling |
-| Checkpoint mmap planner | Proving that checkpoint tensors can represent the required runtime layout before ordinary DiT materialization is skipped | Host weight cache identity or transfer policy |
-| `host_weight_cache` | Normalizing final ordinary-loader tensors into an immutable node-local entry and returning a validated plan | Model mutation, host registration, H2D, or collectives |
-| `HostWeightPlan` | The one-way loader-to-offloader contract: backing kind, tensor bindings, source coverage, and any bounded transforms | Cache construction or runtime scheduling |
-| `ModuleDiscovery` and model `OffloadPlan` | Discovering DiT/components and declaring block, resident, and on-demand paths | Loader or cache behavior embedded in model pipelines |
-| `DistributedLayerwiseOffloadBackend` | Consuming the exact plan, selecting the existing group, mapping/repointing storage, allocating shared buffers and streams, optional host registration, installing hooks, and cleanup | Re-running loader compatibility checks or orchestrating replicas |
-| `DistributedLayerwiseOffloadHook` | The block hot path: prefetch, optional DLO AllGather, readiness events, parameter repointing, and replacement with offload placeholders | Cache keys, manifest validation, or request routing |
-| `host_registration` and platform backend | All-or-nothing registration lifecycle for existing immutable mappings | Choosing which weights are cached or changing the two-slot device-buffer contract |
-| `DiffusionWorker` | Calling backend teardown before destroying the distributed environment | Managing backend-owned mappings or registrations directly |
-
-The configuration fields are routed to the component that can enforce their
-contract. The cache root and lock timeout are consumed by the loader.
-`dlo_use_host_weight_cache` explicitly selects final-layout cache publication
-instead of direct-checkpoint mmap. It defaults to false so the ordinary
-pinned-loader path remains available without requiring an opt-out.
-With `pin_cpu_memory=True`, selecting the host weight cache deliberately chooses
-the transform-complete layout even when direct checkpoint mmap is available,
-because only that final layout can be registered without deferred transforms.
-The backend consumes `dlo_host_weight_cache_pin_limit_gib` as an optional
-registration ceiling; zero applies no additional ceiling and does not select
-the cache by itself.
-`dlo_use_allgather` controls loader plan routing because the host weight cache
-is no-AllGather-only, and it selects the backend transfer protocol. This is
-coordination through typed configuration and `HostWeightPlan`, not through
-model-specific global state.
-
-### Runtime host-layout identity (v1)
-
-Host weight cache v1 identifies the loader-produced host representation, not
-the topology used later to move weights to devices. These are separate
-contracts:
-
-- `RuntimeHostLayout` describes tensor ownership and representation after the
-  ordinary loader has applied its parallel-aware transforms. This is the
-  layout stored in the host weight cache.
-- `DLOTransferLayout` describes whether each worker copies its local weights
-  directly or participates in the existing DLO AllGather protocol. It is not
-  part of the host weight cache in v1; AllGather continues to use its existing
-  process-owned host shards.
-
-The v1 physical-sharing invariant is one cache entry per TP coordinate,
-shared by equivalent DP and SP consumers on the same node. The identity
-therefore includes TP world size and TP rank, because different TP coordinates
-own different tensors. It excludes DP world size/rank and SP rank, because
-those dimensions do not change the final tensor ownership supported by v1.
-SP world size and implementation details remain conservative compatibility
-guards until equivalent layouts across those configurations are proven.
-
-PP, HSDP/DTensor, expert parallelism, and CFG parallelism are rejected rather
-than assigned speculative cache identities. Supporting one of these layouts
-requires first defining its loader-produced ownership, representation, and
-sharing domain; it does not follow automatically from supporting its runtime
-collectives. Consequently, this phase implements a TP-coordinate runtime host
-layout, not a general parallel-layout cache.
-
-### Shared startup and lifecycle
-
-The shared control path selects host backing and transfers its ownership to
-the backend before either transfer mode installs hooks:
-
-```mermaid
-sequenceDiagram
-    participant C as OmniDiffusionConfig
-    participant R as DiffusionModelRunner
-    participant L as DiffusersPipelineLoader
-    participant B as DLO backend
-    participant W as DiffusionWorker
-
-    C->>R: validated offload policy
-    R->>L: load_model(config)
-    L->>L: select and validate host backing
-    L-->>R: pipeline + optional HostWeightPlan
-    R->>B: enable(config, device, optional plan)
-    B->>B: discover modules and consume host backing
-    B-->>R: mode-specific backend is ready
-    W->>B: shutdown calls disable()
-    B->>B: synchronize, unregister, remove hooks, close mappings
-```
-
-The loader returns a plan only after its contract is valid: checkpoint plans
-have preflight-proven source coverage and metadata, while host weight cache
-plans also have full manifest, file, and mapped-content validation. The runner
-then transfers ownership exactly once with
-`model_loader.take_host_weight_plan()`. If no plan is returned, the backend
-requires materialized ordinary-loader tensors. If a plan caused ordinary DiT
-materialization to be skipped, failure to create or enable its consumer is a
-hard startup error rather than a fallback to uninitialized parameters.
-
-At inference time, the model pipeline remains the caller: normal block forward
-calls activate the installed hooks. Both modes use one copy stream and two
-shared device slots sized for the largest streamed block. The hooks do not
-call the loader or cache.
-
-### AllGather startup and hot path
-
-```mermaid
-sequenceDiagram
-    participant B as DLO backend
-    participant H as block hooks
-    participant G as existing DP or SP group
-
-    B->>B: choose existing group and prepare persistent local shards
-    B->>B: allocate two device slots and a communication stream
-    B->>H: install hooks and prime the first block
-    loop each streamed block
-        H->>H: copy the next local shard to the alternate slot
-        H->>G: all_gather_into_tensor(next block)
-        G-->>H: complete next block is ready
-        H->>H: execute current block and rotate slots
-    end
-```
-
-AllGather uses the existing DP group when DP is greater than one, otherwise
-the existing SP group when SP is greater than one. TP is never selected as
-DLO's weight-sharding group. Every participating rank must request blocks in
-the same order; fast P2P reduces communication cost but does not remove that
-lockstep contract. An effective group size of one issues no collective and
-uses rank-local transfer.
-
-### No-AllGather startup and hot path
-
-```mermaid
-sequenceDiagram
-    participant L as DiffusersPipelineLoader
-    participant C as host_weight_cache
-    participant B as DLO backend
-    participant P as platform registration
-    participant H as block hooks
-
-    alt host weight cache explicitly selected
-        L->>L: load, mutate, validate, and calibrate final tensors
-        L->>C: publish or join final-layout entry
-        C-->>L: validated host weight cache plan or fail-closed fallback
-        L-->>B: cache plan or materialized fallback via runner
-    else automatic checkpoint selection
-        L->>L: checkpoint mmap preflight
-        alt loader selects a compatible checkpoint plan
-            L-->>B: checkpoint plan via runner
-        else ordinary loading
-            L->>L: load, mutate, validate, and calibrate final tensors
-            L-->>B: materialized tensors via runner
-        end
-    end
-    B->>B: consume complete rank-local block sources
-    opt host weight cache plan and pinned-memory policy
-        B->>P: register the complete immutable mapping
-        P-->>B: direct-H2D source or bounded-staging fallback
-    end
-    B->>H: install hooks and prime the first block
-    loop each streamed block
-        alt registered host weight cache mapping
-            H->>H: copy complete next block directly to device
-        else pageable mmap mapping
-            H->>H: pack into a bounded staging slot, then copy to device
-        else ordinary runtime tensors
-            H->>H: copy complete next block to device
-        end
-        H->>H: execute current block and rotate slots
-    end
-```
-
-No-AllGather creates no DLO weight group and issues no DLO collective.
-Workers may therefore schedule requests independently. Ordinary TP and SP
-collectives remain inside model execution; host registration changes only
-the source of the complete-block H2D copy.
-
-### Host backing and transfer composition
-
-| Loader-selected host backing | Backend transfer mode | Setup result | Per-block hot path |
-| --- | --- | --- | --- |
-| Ordinary runtime tensors | AllGather group larger than one | Each rank retains a pinned local weight shard | shard H2D → DLO AllGather → device slot |
-| Checkpoint mmap | AllGather group larger than one | Each rank prepares its persistent shard, then releases the source mapping | shard H2D → DLO AllGather → device slot |
-| Ordinary runtime tensors | Rank-local / no-AllGather | Each rank retains the complete loader-produced local layout | complete-block H2D → device slot |
-| Checkpoint mmap, or host weight cache with pinned memory disabled/registration unavailable | Rank-local / no-AllGather | Immutable mapping plus two bounded host-staging slots | mmap views → staging slot → device slot |
-| Host weight cache mmap with pinned memory enabled and registration available | Rank-local / no-AllGather | Complete mapping is registered by the platform backend | registered mmap views → device slot |
-
-The three axes therefore remain independent:
-
-1. **Offload policy:** whether DLO is enabled and which layers are resident.
-2. **Host backing:** ordinary tensors, checkpoint mmap, or final host weight cache
-   mmap, selected by the loader.
-3. **Transfer protocol:** rank-local H2D or H2D plus DLO AllGather, selected by
-   the backend from validated configuration and existing topology.
-
-### Failure and lifecycle boundaries
-
-- Invalid option combinations fail during configuration validation.
-- Checkpoint mmap preflight failure falls back before ordinary loading is
-  skipped.
-- Host weight cache incompatibility, timeout, or publication failure retains
-  the already-valid ordinary tensors.
-- Host-registration failure rolls back and retains bounded staging. A rollback
-  failure aborts startup because unmapping a range still owned by the platform
-  would be unsafe.
-- During shutdown, DLO synchronizes outstanding device work, unregisters host
-  mappings, removes hooks, and closes mmap handles only after safe unregister;
-  the worker destroys its process groups afterward.
-
-DLO deliberately does not own DP replica placement, independent-engine request
-routing, cross-engine admission control, or cross-node cache distribution.
-Those belong to an external router/orchestrator. It also does not replace TP or
-SP model collectives; it only adds an optional weight collective over an
-already-existing DP or SP group.
-
-## Shared design
-
-The following topology and loader contracts apply to both transfer modes.
 
 ### DLO consumes the existing parallel topology
 
@@ -335,10 +65,15 @@ DLO does not create a new DP, TP, or SP topology. It reads the configured
 `DiffusionParallelConfig` and attaches offload hooks to the DiT blocks after the
 standard distributed groups have been initialized.
 
-AllGather mode selects one existing group for DLO weight sharding; no-AllGather
+The DLO weight-sharding group is selected as follows:
-mode creates no DLO group. Neither mode changes ordinary TP or SP model
+
-collectives. The mode-specific group and scheduling contracts are described
+1. Use the existing DP group when `data_parallel_size > 1`.
-below.
+2. When DP is one and SP is greater than one, use the SP group.
+3. Otherwise, run rank-locally without a DLO process group.
+
+TP is deliberately not used as DLO's AllGather group. HSDP has its own
+parameter-sharding lifecycle and is not allowed to be sharded a second time by
+DLO's AllGather path.
 
 ### The loader owns host-weight planning
 
@@ -370,24 +105,32 @@ flags such as `_supports_mmap_loading` or parameter attributes for mmap-only
 transforms. Model-specific direct-layout knowledge, when required, lives in a
 checkpoint adapter beside the ordinary loader.
 
-## AllGather design
+### Phase-I normalized online-FP8 cache
 
-### Group selection
+The direct-checkpoint plan above cannot quantize a BF16 source online. Phase I
+adds an explicit, opt-in normalized cache for the narrower case of one TP1
+transformer with per-tensor online FP8 and DLO AllGather:
 
-The DLO weight-sharding group is selected in this order:
+```text
+cache miss:  BF16 source -> ordinary online FP8 loader -> atomic FP8 cache
+cache hit:   FP8 cache   -> direct mmap plan -> DLO host shard -> AllGather
+```
 
-1. Use the existing DP group when `data_parallel_size > 1`.
+The cache stores the serialized runtime representation, including finalized FP8
-2. When DP is one and SP is greater than one, use the existing SP group.
+weights, generated scales, and persistent transformer buffers. Its manifest
-3. Otherwise, use an effective group size of one and issue no collective.
+records the schema, component, quantization contract, tensor count, source
+files, and a source fingerprint. A missing, stale, incomplete, incompatible, or
+invalid artifact is a cache miss; cache publication is best-effort and falls
+back to the ordinary loader on failure.
 
-TP is deliberately not used as DLO's AllGather group. HSDP has its own
+Phase I keeps the cache binding deliberately simple: one source per transformer
-parameter-sharding lifecycle and cannot be sharded a second time by DLO.
+component, with existing per-tensor transforms applied before publication. The
+cache is not selected for DLO-disabled, no-AllGather, TP>1, or HSDP deployments,
+and it normalizes the transformer only. Cache sharing and compatibility for
+other components, transformed TP coordinates, and broader DP/SP layouts remain
+in [RFC #6231](https://github.com/vllm-project/vllm-omni/issues/6231).
 
-### Sharded transfer and double buffering
+### AllGather path
-
-The loader may provide ordinary runtime tensors or a compatible checkpoint
-plan. During backend setup, either source is normalized into persistent local
-shards. The final-layout host weight cache is not selected in AllGather mode.
 
 With the default `dlo_use_allgather=True`, each rank stores approximately
 `1 / group_size` of each streamable block in pinned host memory. The next
@@ -395,15 +138,6 @@ block's shard is copied to a device buffer and reconstructed with
 `all_gather_into_tensor` on a communication stream while the current block is
 executing.
 
-![AllGather DLO sharded host weights and lockstep reconstruction](../../figures/dlo/dlo_allgather_sharded_host.svg)
-
-The figure uses DP2 with TP2 to expose both parallel dimensions. DLO creates
-one AllGather group across the DP replicas for each TP coordinate; TP0 workers
-reconstruct TP0 blocks together and TP1 workers reconstruct TP1 blocks
-together. Ordinary TP collectives remain inside each DP replica. Unlike the
-no-AllGather host weight cache path, these persistent pinned host shards are
-process-owned, and every participating rank must follow the same block order.
-
 ```text
 Compute:    [Block N]             [Block N+1]          [Block N+2]
 H2D:                      [shard N+1]           [shard N+2]
@@ -429,29 +163,11 @@ When DP is greater than one, the engine can process one request per DP rank in
 the same denoising wave. Because AllGather is a collective, all participating
 requests must take the same execution path at every denoising step.
 
-Fast NVLink/NVSwitch changes collective cost, not this execution contract. For
+### Rank-local path without DLO AllGather
-independently scheduled replicas—even on one NVSwitch baseboard—no-AllGather
-plus shared mmap storage avoids coupling their request schedules and failure
-domains.
-
-## No-AllGather design
-
-### Rank-local transfer
 
 With `--dlo-no-use-allgather`, DLO forces its internal offload shard size to
 one and streams complete blocks using H2D copies only. The host backing may be
-a loader-approved checkpoint mapping, a normalized host weight cache mapping, or
+either a loader-approved checkpoint mapping or ordinary runtime tensors.
-ordinary runtime tensors.
-
-![No-AllGather DLO shared host weights and independent runtime orchestration](../../figures/dlo/dlo_no_allgather_shared_host.svg)
-
-The figure uses TP2 to make per-coordinate physical sharing explicit. Each
-engine contains two rank-local workers and devices. The cache keeps one
-immutable entry per TP coordinate, so equivalent TP0 workers across engines
-share the TP0 entry and equivalent TP1 workers share the separate TP1 entry.
-An external router or orchestrator chooses each engine's requests; DLO
-coordinates only the worker-local mapping, hooks, transfer streams, events,
-and device slots.
 
 For direct mmap, each process retains immutable safetensors views and uses two
 bounded pinned host staging slots. Processes on the same node that map the same
@@ -460,17 +176,10 @@ the persistent private full-model copy per pure-DP process, but each process
 still packs and transfers every complete block. Sharing is node-local; each
 node has its own page cache.
 
-For a host weight cache mapping, the default pinned-memory policy attempts to
+When direct mmap preflight fails, the regular model loader remains responsible
-replace those host slots with direct copies from the shared final tensor views.
+for preparing each rank's weights, including TP-local tensors or HSDP-managed
-Disabling pinned memory or a failed registration retains staging behavior.
+parameters. In that fallback, each pure-DP process keeps a private full runtime
-
+copy.
-When direct mmap preflight fails or the host weight cache is selected, the
-regular model loader remains responsible for preparing each rank's weights,
-including TP-local tensors or HSDP-managed parameters. Ordinary pinned tensors
-remain the default without cache selection. When the host weight cache is
-explicitly selected,
-supported CPU layouts enter the cache; unsupported layouts keep one private
-runtime copy per process.
 
 This mode means:
 
@@ -481,109 +190,18 @@ This mode means:
 - TP/HSDP/SP collectives, if configured, are not disabled by this flag; only
   DLO's additional weight AllGather is disabled.
 - Pure DP deployments share one checkpoint-backed copy per node when direct
-  mmap is selected. The host weight cache can share equivalent ordinary-loader
+  mmap is selected; the ordinary-loader fallback keeps one private runtime
-  outputs; its fallback keeps one private runtime copy per rank.
+  copy per rank.
 - The scheduler does not require a synchronized DP request wave for DLO.
 
-### Post-load host weight cache
-
-The Phase B cache preserves the same ownership boundary. DLO neither derives
-cache keys nor interprets model loaders. No-AllGather DLO first attempts a
-compatible direct-checkpoint mmap plan. When that plan is unavailable, every
-rank completes the ordinary loader, custom callbacks, post-load processing,
-strict validation, and calibration. When cache use or registration is
-explicitly selected, the loader:
-
-1. discovers final CPU DiT parameters and persistent buffers;
-2. rejects unsupported non-contiguous, aliased, quantized, distributed, or
-   ambiguous layouts before changing the model;
-3. hashes tensor names, metadata, and final bytes;
-4. builds or joins one content-addressed entry under a strict POSIX lock; and
-5. returns a `HostWeightPlan(backing_kind="host_weight_cache")` only after file,
-   manifest, and remapped-tensor content validation succeeds.
-
-The identity excludes DP size/rank and SP rank. It includes TP world size/rank
-and conservative SP implementation/world-size guards. DP replicas and
-equivalent SP ranks can therefore share; different TP coordinates cannot.
-Final tensor content is authoritative, so an unmodeled loader/config change
-that changes bytes creates a different entry instead of risking silent
-sharing.
-
-Publication writes sharded safetensors and a JSON manifest into a same-
-filesystem temporary directory, flushes them, and atomically renames the
-directory. The manifest includes per-file SHA-256 digests and the final tensor
-content digest. Kernel `flock` ownership distinguishes a live writer from a
-stale lock file: process death releases the lock automatically. Stale temporary
-entries are removed only by the next lock owner. A bounded wait or any cache
-error retains the already-valid ordinary tensors.
-
-DLO maps this plan without moving the DiT to `meta` and without rerunning any
-post-load hook. It changes tensor storage while preserving `Parameter` objects,
-then runs the existing `gc.collect()`/`malloc_trim()` cleanup before installing
-streaming hooks. The source mappings remain open through DLO disable. With
-pinned memory disabled, or when registration is unavailable, they feed the
-existing bounded two-slot host-staging path. Every streamed block is first
-packed from pageable mmap storage into a staging slot and is then copied to the
-device. This removes model-sized private pinned storage, but adds a recurrent
-host-to-host copy and can amplify TP synchronization waits when several ranks
-compete for host bandwidth.
-
-This version is deliberately steady-state-oriented. It requires roughly one
-local-disk model copy per runtime identity, has no eviction, and still performs
-ordinary loading plus full content-hash passes in every rank. Cache publication
-or validation failure falls back to the already-valid ordinary host tensors.
-Skip-load-on-hit is a separate follow-up after loader side effects can be
-modeled safely.
-
-### Registered host weight cache transfer
-
-Selecting the host weight cache with `pin_cpu_memory=True` opts a worker into
-registration of the complete mapping when its platform supports it. It also
-changes loader selection: no-AllGather uses the ordinary loader and final-layout
-host weight cache even when a TP1 direct-checkpoint plan is available. A
-checkpoint binding can carry a deferred adapter such as
-grouped-QKV reordering. Registering that raw source would not remove the
-adapter's recurrent CPU allocation/copy, whereas the host weight cache contains
-the transform-complete final tensors.
-
-After mapping and validating every final tensor, DLO groups source storages by
-backing file, page-aligns their address ranges, and coalesces overlapping or
-adjacent ranges within that mapping. When a positive per-worker ceiling is
-configured, the complete byte count is checked against it before any platform
-mutation. The CUDA backend
-first requires `cudaDevAttrHostRegisterReadOnlySupported`, then registers every
-range with the read-only host-registration flag and verifies that PyTorch
-recognizes every mapped tensor as pinned. A device without that capability
-retains bounded staging; vLLM-Omni does not replace immutable mappings with a
-writable or copy-on-write CUDA-specific layout. Other registration failures
-roll back any ranges already registered and preserve the existing two-slot
-staging path. Partial direct transfer is deliberately unsupported. A rollback
-failure aborts worker initialization so a still-registered range is retained
-until process/context teardown rather than being unsafely unmapped.
-
-On success, the hooks copy each immutable tensor view directly into its offset
-in the existing flattened rotating device buffer on the copy stream. Parameter
-repointing, two-block HBM residency, and H2D/compute overlap are unchanged. The
-direct path trades recurrent host packing for finer-grained H2D operations. A
-packed cache schema should be considered only if that additional format and
-publication complexity becomes necessary.
-
-Registration is per process and platform context; physical file-cache pages
-remain shared and are not copied into one private host allocation per worker.
-The operator must nevertheless budget page-locked memory and satisfy the
-platform and OS registration limits for every worker. During shutdown, DLO
-first synchronizes outstanding device work, unregisters all ranges, and then
-closes the safetensors mappings. Worker teardown invokes this cleanup before
-destroying the distributed environment and platform context.
-
 ## Parallelism compatibility
 
 | Parallelism | DLO + AllGather | DLO without AllGather |
 |---|---|---|
-| **DP** | Supported primary path. DLO shards host weights across the DP group and can run DP multi-concurrency. | Supported rank-local path. Compatible TP1 replicas can share checkpoint pages; the optional host weight cache shares equivalent final layouts while excluding DP rank from its identity. CUDA workers may register the complete final mapping for direct H2D. |
+| **DP** | Supported primary path. DLO shards host weights across the DP group and can run DP multi-concurrency. | Supported rank-local path. Compatible TP1 replicas can share checkpoint pages on each node; fallback runtime tensors remain private. |
-| **SP** | Supported in the implementation. With DP=1, DLO uses the SP group for host-weight sharding; SP still shards sequence/activation work. | SP remains active without a DLO weight collective. Host weight cache v1 excludes SP rank and includes conservative SP implementation/world-size guards. Registered transfer does not remove ordinary SP activation/attention collectives. |
+| **SP** | Supported in the implementation. With DP=1, DLO uses the SP group for host-weight sharding; SP still shards sequence/activation work. | SP remains active, but DLO keeps standard-loader rank-local weights and adds no SP weight collective. |
-| **TP > 1** | Outside the Phase A direct-mmap scope. The loader preserves TP-local layouts and DLO may apply DP/SP host sharding to ordinary runtime tensors. | The ordinary TP-aware loader produces rank-local tensors. Host weight cache v1 creates one entry per TP coordinate, shareable by equivalent DP/SP processes. Registering those final mappings avoids the recurrent staging copy but does not remove ordinary TP collectives. |
+| **TP > 1** | Outside the Phase A shared-mmap support scope. The loader falls back before mutation, preserves TP-local layouts, and DLO may apply DP/SP host sharding to those ordinary runtime tensors. | Outside the Phase A shared-mmap support scope. The ordinary TP-aware loader produces rank-local tensors, which DLO streams without an additional weight collective; DP replicas retain private runtime storage. |
-| **HSDP** | Rejected. HSDP has already sharded parameters, so DLO AllGather would double-shard them. | Accepted only with ordinary-loader rank-local tensors. HSDP owns parameter sharding and its own gathers; host weight cache v1 rejects HSDP/DTensor layouts. |
+| **HSDP** | Rejected. HSDP has already sharded parameters, so DLO AllGather would double-shard them. | Accepted by configuration. HSDP owns parameter sharding and its own gathers; DLO only stages rank-local parameters. End-to-end coverage is limited. |
 
 ### Combined dimensions
 
@@ -599,9 +217,7 @@ destroying the distributed environment and platform context.
 - **HSDP + DP or TP:** rejected independently by the diffusion parallel
   configuration.
 
-## Mode constraints
+## Request and loading constraints
-
-### AllGather constraints
 
 AllGather DP multi-concurrency requires:
 
@@ -609,54 +225,109 @@ AllGather DP multi-concurrency requires:
 - the same `num_inference_steps` for all requests in a wave; and
 - identical request arguments that affect the collective execution path.
 
-Per-tensor online FP8 linears can use DLO AllGather after the ordinary loader
-finalizes their runtime weights and scales. Other online methods must use
-`--dlo-no-use-allgather` or disable online quantization until their runtime
-layouts are validated.
-
-### No-AllGather constraints
-
 The no-AllGather path does not impose these DLO-specific synchronized-wave
-requirements. Host weight cache v1 is no-AllGather only and rejects all
+requirements.
-quantization configurations, HSDP/DTensor, expert parallelism, CFG
-parallelism, PP, non-contiguous tensors, and tied/shared storage.
-
-### Shared loading constraint
 
 Direct checkpoint mmap can back either transfer path. It is currently limited
 to proven TP1, non-HSDP, non-online-quantized layouts. Other layouts use the
-ordinary loader.
+ordinary loader. Per-tensor online FP8 linears can use DLO AllGather after the
+ordinary loader finalizes their runtime weights and scales; DLO then shards and
+reconstructs those tensors with their recorded layouts. Other online methods
+must use `--dlo-no-use-allgather` or disable online quantization until their
+runtime layouts are validated.
 
-## Validation status
+The Phase-I normalized online-FP8 cache is the supported TP1 transformer case.
+The general cache-compatibility contract for additional components, transformed
+TP coordinates, and cross-DP/SP reuse remains in
+[RFC #6231](https://github.com/vllm-project/vllm-omni/issues/6231).
 
-Historical test counts and hardware measurements are intentionally not kept
+## Validation coverage
-in this design document because they can drift as the implementation changes.
+
-Revalidation must run against the final commit and publish commit-pinned
+Current source-level validation includes:
-artifacts before making hardware or performance claims. Track that work in
+
-[issue #6231](https://github.com/vllm-project/vllm-omni/issues/6231).
+- HSDP + DLO + AllGather rejection;
+- HSDP + DLO without AllGather acceptance at configuration level;
+- loader preflight fallback for TP, HSDP, online quantization, unknown custom
+  loaders, missing keys, and shape/dtype mismatches;
+- ordinary-loader fallback for per-tensor online FP8 linears followed by DLO
+  sharding of finalized weights and scales;
+- exact loader-to-backend plan transfer and ordinary-loader fallback;
+- rank-local mmap source retention, bounded two-slot staging, and adapter
+  transforms without parameter-side flags;
+- resident-layer requests requiring no-AllGather;
+- DP request-wave validation for denoising-step compatibility;
+- sharding, double-buffer, AllGather-size, and heterogeneous-block regression
+  tests.
+
+### B300 parallel-topology smoke matrix
+
+A four-GPU B300 smoke test covered MiniMax-H3 FL2VA with the same prompt, seed,
+CUDNN attention backend, 256x256 output, two denoising steps, and
+`dlo_resident_layers=0`. The TP2 rows used DiT DP2xTP2 with the text encoder and
+VAEs at TP1. They validate the ordinary-loader fallback only, not direct mmap
+or shared-mmap host-memory savings.
+
+| Configuration | Result | Warm E2E | Peak device memory | Host PSS |
+|---|---:|---:|---:|---:|
+| DP4xTP1 AllGather | Passed, 4 concurrent requests | 2.87 s / 4 requests | 13.84 GiB | 211.99 GiB |
+| DP4xTP1 no-AllGather | Passed, 1 request | 15.02 s | 13.23 GiB | 187.77 GiB |
+| DP2xTP2 AllGather | Passed, 2 concurrent requests | 4.16 s / 2 requests | 12.50 GiB | 211.97 GiB |
+| DP2xTP2 no-AllGather | Passed, 1 request | 3.51 s | 11.88 GiB | 314.01 GiB |
+
+Within each topology, the AllGather and no-AllGather video and audio outputs
+were byte-identical. All four runs completed without an `ERROR` or traceback
+and released their device allocations. For DP4xTP1, no-AllGather direct mmap
+reduced total PSS by 24.22 GiB (11.4%) and `Private_Dirty` from 211.33 to
+125.32 GiB (40.7%) relative to AllGather. For DP2xTP2, preflight selected the
+ordinary loader as designed; no-AllGather PSS was 314.01 GiB, about 48% above
+AllGather, because DP replicas did not share checkpoint-backed runtime
+weights. This is a functional and memory smoke test, not a production-quality
+performance or output-quality benchmark.
+
+### Host-memory measurement
+
+A two-worker MiniMax-H3 FL2VA measurement on one L20X node compared the
+ordinary-loader fallback with direct mmap. Both runs used
+DP=2, TP=1, no DLO AllGather, BF16 weights, two denoising steps, and a
+256x256 four-second request. The ordinary-loader workers were sampled after
+initialization. The mmap workers were sampled after one completed request, so
+the checkpoint working set had been faulted into the page cache; this is the
+more conservative point for mmap.
+
+The values below come from `/proc/<worker>/smaps_rollup` and include the whole
+worker, not only the DiT. The stable rank-to-rank difference comes from other
+pipeline components, so each worker should be compared with the same worker in
+the other storage mode.
+
+| Worker | Ordinary RSS | mmap RSS | Ordinary PSS | mmap PSS | PSS reduction |
+|---|---:|---:|---:|---:|---:|
+| DP worker 0 | 168.27 GiB | 132.76 GiB | 167.84 GiB | 101.43 GiB | 66.40 GiB |
+| DP worker 1 | 116.19 GiB | 79.97 GiB | 115.73 GiB | 48.64 GiB | 67.09 GiB |
+| **Two-worker total** | — | — | **283.56 GiB** | **150.08 GiB** | **133.48 GiB (47.1%)** |
+
+The direct-mmap workers each reported 62.45 GiB `Shared_Clean` but only
+31.20 GiB `Pss_File`, which is the proportional charge expected when the same
+resident checkpoint pages are mapped by two workers. `Private_Dirty` also fell
+from 167.53 to 70.24 GiB for worker 0 and from 115.40 to 17.44 GiB for worker
+1, a reduction of about 97–98 GiB per worker. RSS understates this benefit
+because it counts a shared physical page in every process that maps it; summed
+PSS is the appropriate node-memory comparison.
+
+The highest-value missing coverage is broader end-to-end numerical and
+lifecycle comparison against ordinary layerwise offload for DP+SP,
+HSDP+SP+no-AllGather, and TP greater than one across additional models and
+target CUDA/NCCL or CANN/HCCL hardware. That broader TP coverage does not
+change the Phase A direct-mmap TP1 support boundary.
 
 ## Recommendations
 
-### AllGather
+- Use **DP + DLO AllGather** for the supported throughput and host-memory
-
+  scaling path.
-- Use **DP + DLO AllGather** when ranks already execute the same block path in
-  lockstep and belong to the same fast P2P domain. It shards persistent host
-  weights across that group.
 - Use **SP + DLO AllGather** for long-sequence workloads when DP concurrency is
   not the goal.
-
-### No-AllGather
-
 - Use **no-AllGather** when independent replica execution is required. TP1
-  direct-mmap deployments can share checkpoint pages per node. Enable the
+  direct-mmap deployments can share checkpoint pages per node; other layouts
-  host weight cache only when independent replicas have repeated final layouts
+  retain the ordinary loader's private host memory behavior and are outside
-  and benchmark the target CPU/memory topology, especially for matching
+  the Phase A shared-mmap support scope.
-  coordinates across multiple TP engines. With the default pinned-memory
+- Prefer **HSDP alone** for production HSDP deployments until the combined
-  policy, supported CUDA workers prefer registered direct H2D. Configure a
+  HSDP + DLO no-AllGather path has broader end-to-end coverage.
-  positive ceiling when an explicit page-locking bound is required, or disable
-  pinned memory when pageable staging is operationally preferable.
-
-### HSDP
-
-- Do not combine HSDP with DLO AllGather or host weight cache v1. HSDP with
-  no-AllGather remains an ordinary-loader rank-local path.

docs/design/figures/dlo/dlo_allgather_sharded_host.svg

@@ -1,165 +0,0 @@
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docs/design/figures/dlo/dlo_no_allgather_shared_host.svg

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-  <desc id="desc">An external orchestrator routes requests to two independently scheduled TP2 diffusion engines on one node. Each engine contains two rank-local workers and devices, with private loaders, mmap views, DLO runtime hooks, streams, events, and rotating GPU slots. Equivalent TP0 workers across engines map one immutable TP0 entry in the host weight cache, while equivalent TP1 workers map a separate TP1 entry, without a cross-engine DLO AllGather.</desc>
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docs/user_guide/diffusion/offloader/distributed_layerwise_offload.md

@@ -1,127 +1,180 @@
 # Distributed Layerwise Offloading
 
-Distributed layerwise offloading (DLO) streams DiT blocks through two device
+Distributed layerwise offloading (DLO) extends block streaming to multi-device
-buffers instead of keeping the complete DiT in HBM. It supports sharded host
+deployments. With AllGather enabled, each rank stores roughly `1 / dp_size` of
-weights with AllGather or independently streamed rank-local weights. The
+the host weights and reconstructs each layer at runtime. Without AllGather,
-no-AllGather path can optionally share node-local host weights through mmap.
+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 [shared DLO component architecture](../../../design/feature/offloader/distributed_layerwise_offload.md#architecture)
+See the [DLO feature design](../../../design/feature/offloader/distributed_layerwise_offload.md)
-for ownership and lifecycle coordination. The mode-specific internals are
+for the implementation contract and compatibility matrix.
-documented separately in the
-[AllGather design](../../../design/feature/offloader/distributed_layerwise_offload.md#allgather-design)
-and
-[no-AllGather design](../../../design/feature/offloader/distributed_layerwise_offload.md#no-allgather-design).
 
-## Choose a mode
+## Execution model
 
-| Mode | Use when | Host weights | Runtime synchronization |
+DLO overlaps three operations with a fixed two-block device buffer:
-| --- | --- | --- | --- |
-| DLO AllGather (default) | DP ranks execute the same block path in lockstep | About `1 / dp_size` per rank | DLO weight AllGather |
-| no-AllGather | Ranks or engines must schedule independently | Complete rank-local layout | No DLO weight collective |
-| no-AllGather + host weight cache | Equivalent independent workers share one node | Shared final mmap layout per TP coordinate | No DLO weight collective |
-| host weight cache + host registration | The platform supports registration and recurrent staging is too expensive | Shared registered mmap layout | No DLO weight collective |
 
-AllGather is normally the best choice for synchronized DP. Host weight cache mode
+```text
-targets independently scheduled replicas, especially repeated TP engines on
+Compute stream:  [Layer N]          [Layer N+1]        [Layer N+2]
-one node.
+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
-# Sharded host weights with DLO AllGather
+# Four ranks with sharded host weights and AllGather
 vllm serve /path/to/model --omni \
   --enable-distributed-layerwise-offload \
   --data-parallel-size 4
 
-# Independently streamed rank-local weights
+# 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
 
-# Share final host weights; prefer registered direct H2D under the pinned-memory policy
+# Sequence parallel deployment
 vllm serve /path/to/model --omni \
   --enable-distributed-layerwise-offload \
-  --dlo-no-use-allgather \
+  --usp 4
-  --dlo-use-host-weight-cache
 
-# Apply an 80 GiB per-worker safety ceiling to the same registration attempt
+# Opt-in normalized online-FP8 cache (TP1 + DLO AllGather)
 vllm serve /path/to/model --omni \
+  --quantization fp8 \
   --enable-distributed-layerwise-offload \
-  --tensor-parallel-size 2 \
+  --dlo-fp8-cache-dir /path/to/node-local/fp8-cache
-  --dlo-no-use-allgather \
-  --dlo-use-host-weight-cache \
-  --dlo-host-weight-cache-pin-limit-gib 80
 ```
 
-## Relevant options
+```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` |
-| `--dlo-no-use-allgather` | Stream complete rank-local blocks independently | `false` |
+| `--data-parallel-size N` | DP ranks and AllGather weight-sharding group | `1` |
-| `--dlo-use-host-weight-cache` | Use the final-layout cache and prefer registration when pinned memory is enabled | `false` |
+| `--dlo-use-allgather` | Shard host weights and reconstruct with AllGather | `true` |
-| `--dlo-host-weight-cache-pin-limit-gib GIB` | Optional per-worker registration ceiling; zero means no additional ceiling | `0` |
+| `--dlo-no-use-allgather` | Stream complete rank-local blocks without a DLO weight collective | `false` |
-| `--dlo-resident-layers N` | Keep eligible leading DiT blocks resident in HBM | `0` |
+| `--dlo-resident-layers N` | Keep N leading main-DiT blocks on device; requires no-AllGather and model-declared resident paths | `0` |
+| `--dlo-fp8-cache-dir PATH` | Opt into the Phase-I normalized online-FP8 cache for the transformer | unset |
 
-No-AllGather DLO automatically uses a compatible checkpoint mmap plan. When
+## Host-weight loading
-checkpoint mmap is unavailable, it preserves ordinary pinned loader weights by
-default. `--dlo-use-host-weight-cache` explicitly selects the transform-complete
-final-layout cache. Under the default `pin_cpu_memory=True` policy, DLO attempts
-to register the complete immutable mapping for direct H2D. Setting
-`pin_cpu_memory=False` through structured or programmatic configuration retains
-the staged mmap path instead. A positive pin limit is only an optional safety
-ceiling and does not select the cache. The cache uses
-`~/.cache/vllm-omni/dlo-host-weights`. Programmatic configuration may override
-`dlo_host_weight_cache_dir` and the writer lock timeout; these advanced storage
-controls are intentionally not separate CLI flags.
 
-## Operational notes
+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.
 
-- All workers that should share must see the same local, disk-backed cache
+The shared-mmap optimization in this phase is supported only with TP1. TP
-  directory. Do not use tmpfs or a cross-node filesystem for this version.
+greater than one falls back before model mutation to ordinary TP-aware loading.
-- Cache entries are immutable and validated before use. A cache or registration
+DLO may still consume those TP-local tensors, but this is a compatibility path:
-  failure keeps the ordinary loader weights or falls back to bounded staging.
+it does not share checkpoint-backed runtime weights across DP replicas and
-- The bounded-staging fallback is a memory-first mode: recurrent pageable-mmap to
+provides no shared-mmap host-memory guarantee.
-  pinned-slot copies can reduce throughput, especially across concurrent TP
-  engines. Use it intentionally by disabling pinned host memory, or when the
-  active platform cannot register the immutable mapping.
-- When a positive registration ceiling is configured, it must cover the
-  complete page-aligned mapping reported in the worker log. Registration is
-  all-or-nothing. Zero applies no additional ceiling.
-- CUDA is the first registration backend and requires read-only host
-  registration support for immutable cache mappings. Platforms or devices
-  without that capability continue to use bounded staging; pinned-memory policy
-  requests direct H2D but does not guarantee it.
-- Registration is process-local but does not duplicate the underlying file
-  pages. Each worker must still satisfy its platform and OS page-locking limits.
-- Shutdown unregisters host ranges before closing their mmap handles.
-- Host weight cache v1 has no automatic eviction. Stop all users of an entry
-  before deleting it.
 
-## Scheduling constraints
+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.
 
-AllGather ranks must request blocks in the same collective order. Concurrent DP
+With direct checkpoint mmap, the loader:
-requests may use different prompts, but they must follow the same denoising and
-block-execution path and set the same explicit `num_inference_steps`.
 
-No-AllGather workers do not have this DLO lockstep requirement. Ordinary TP or
+1. saves non-persistent buffers such as RoPE frequencies;
-SP model collectives still synchronize ranks within each engine.
+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.
+
+## Normalized online-FP8 cache
+
+`--dlo-fp8-cache-dir` is an opt-in Phase-I cache for per-tensor online FP8 with
+TP1 and DLO AllGather. On a cache miss, the ordinary loader quantizes the
+transformer once and publishes finalized FP8 weights and scales as an atomic,
+serialized safetensors artifact. On a valid cache hit, the loader selects that
+serialized source before DLO planning, so the transformer can be loaded through
+the direct checkpoint-mmap path without re-materializing the full BF16 source.
+
+The cache is keyed to the source files through the manifest's schema, component,
+quantization contract, and source fingerprint. Incomplete, incompatible, or
+stale artifacts are treated as cache misses. Cache creation is best-effort: a
+publication failure logs a warning and keeps the ordinary online-FP8 path.
+
+Phase I normalizes the transformer only. Other pipeline components keep their
+ordinary loading and quantization behavior. The cache is not selected when DLO
+is disabled, AllGather is disabled, TP is greater than one, or HSDP is enabled.
+
+## 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. The host weight cache can share
+- Direct checkpoint mmap currently requires TP1. TP greater than one is
-  final ordinary-loader layouts between matching TP coordinates at TP1 or TP>1.
+  outside the Phase A shared-mmap support scope and falls back before model
+  mutation to the ordinary TP-aware loader. DLO can stream that runtime layout,
+  but it provides no shared-mmap host-memory benefit or guarantee.
+- 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. Other online quantization methods require no-AllGather
+  DLO transfer path. With AllGather, every rank temporarily materializes the
-  until their runtime layouts are validated.
+  complete FP8 model in host memory before DLO retains only its shard. Other
-- Host weight cache v1 rejects quantized, non-contiguous, aliased/tied,
+  online quantization methods require no-AllGather until their runtime layouts
-  device-only, HSDP, expert-parallel, CFG-parallel, and PP layouts.
+  are validated.
-- HSDP plus DLO AllGather is unsupported, and host weight cache v1 rejects HSDP.
+- Resident leading layers require `--dlo-no-use-allgather` and a model
-- The host weight cache changes steady-state host backing, not startup loading:
+  `OffloadPlan` that declares eligible `resident_dit_paths`.
-  each worker still performs ordinary loading and full content validation.
+- DP concurrency requires an explicit, identical inference-step count.
-- Skip-load-on-hit, cache eviction, and cross-node sharing remain follow-up
+
-  work in
+Sharing transformed TP or quantized runtime layouts beyond this TP1 transformer
-  [RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195).
+cache remains a follow-up design in
+[RFC #6231](https://github.com/vllm-project/vllm-omni/issues/6231).
 
 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.

docs/user_guide/diffusion/parallelism/hsdp.md

@@ -152,7 +152,8 @@ In `DiffusionParallelConfig`:
 **Constraints:**
 
 - `hsdp_replicate_size × hsdp_shard_size == world_size`
-- HSDP cannot be used with Tensor Parallelism (`tensor_parallel_size` must be 1)
+- HSDP cannot be used with Tensor Parallelism, ordinary Data Parallelism,
+  Pipeline Parallelism, or Expert Parallelism.
 
 ---
 

tests/config/test_omni_config.py

@@ -733,8 +733,7 @@ def test_diffusion_parallel_config_matches_diffusion_parallel_world_size_for_vae
 
 def test_diffusion_parallel_config_supports_diffusion_hsdp_auto_sharding():
     cfg = OmniStageDiffusionParallelConfig(
-        pipeline_parallel_size=2,
+        ulysses_degree=4,
-        ulysses_degree=2,
         use_hsdp=True,
         hsdp_shard_size=-1,
         hsdp_replicate_size=2,
@@ -745,10 +744,10 @@ def test_diffusion_parallel_config_supports_diffusion_hsdp_auto_sharding():
 
 
 def test_diffusion_parallel_config_rejects_hsdp_with_tp_or_dp():
-    with pytest.raises(ValueError, match="cannot be used with TP or DP"):
+    with pytest.raises(ValueError, match="not compatible with TP"):
         OmniStageDiffusionParallelConfig(tensor_parallel_size=2, use_hsdp=True, hsdp_shard_size=2)
 
-    with pytest.raises(ValueError, match="cannot be used with TP or DP"):
+    with pytest.raises(ValueError, match="not compatible with DP"):
         OmniStageDiffusionParallelConfig(data_parallel_size=2, use_hsdp=True, hsdp_shard_size=2)
 
 
@@ -908,52 +907,6 @@ def test_structured_diffusion_config_rejects_invalid_compile_granularity():
         omni_config_module._DiffusionConfigProjection(diffusion_compile_granularity="block")
 
 
-@pytest.mark.parametrize(
-    ("enable_dlo", "use_allgather"),
-    [(False, False), (True, True)],
-)
-@pytest.mark.parametrize(
-    ("field_name", "field_value"),
-    [
-        ("dlo_use_host_weight_cache", True),
-        ("dlo_host_weight_cache_pin_limit_gib", 1.0),
-    ],
-)
-def test_structured_diffusion_config_restricts_host_weight_cache_to_no_allgather_dlo(
-    enable_dlo,
-    use_allgather,
-    field_name,
-    field_value,
-):
-    with pytest.raises(ValueError, match="requires no-AllGather distributed layerwise offload"):
-        omni_config_module._DiffusionConfigProjection(
-            enable_distributed_layerwise_offload=enable_dlo,
-            dlo_use_allgather=use_allgather,
-            **{field_name: field_value},
-        )
-
-
-def test_structured_diffusion_config_requires_cache_for_registration_ceiling():
-    with pytest.raises(ValueError, match="requires dlo_use_host_weight_cache=True"):
-        omni_config_module._DiffusionConfigProjection(
-            enable_distributed_layerwise_offload=True,
-            dlo_use_allgather=False,
-            dlo_host_weight_cache_pin_limit_gib=1.0,
-        )
-
-
-@pytest.mark.parametrize(
-    "field_name",
-    [
-        "dlo_host_weight_cache_lock_timeout",
-        "dlo_host_weight_cache_pin_limit_gib",
-    ],
-)
-def test_structured_diffusion_config_rejects_non_finite_host_weight_cache_values(field_name):
-    with pytest.raises(ValueError, match=rf"{field_name} must be finite"):
-        omni_config_module._DiffusionConfigProjection(**{field_name: float("inf")})
-
-
 def test_from_pipeline_config_matches_stage_config_to_omegaconf_behavior_for_representative_stage():
     pipeline = _resolve_pipeline_or_skip("qwen3_tts")
     legacy_stage = merge_pipeline_deploy(pipeline, _load_default_deploy(pipeline))[0]

tests/diffusion/distributed/test_distributed_vae_executor.py

@@ -74,14 +74,6 @@ def mock_dist(monkeypatch: pytest.MonkeyPatch):
     monkeypatch.setattr(dist, "broadcast", lambda *args, **kwargs: None)
 
 
-@pytest.fixture(autouse=True)
-def mock_dit_group(monkeypatch: pytest.MonkeyPatch):
-    monkeypatch.setattr(
-        "vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor.get_dit_group",
-        lambda: None,
-    )
-
-
 @pytest.fixture(autouse=True)
 def mock_dist_vae_executor(monkeypatch: pytest.MonkeyPatch):
     monkeypatch.setattr(DistributedVaeExecutor, "gather_tensors", lambda self, x: [x])

tests/diffusion/distributed/test_expert_parallel_layout.py

@@ -92,9 +92,8 @@ def test_moe_ep_maps_diffusion_sp_cfg_dp_to_vllm_groups(monkeypatch):
     monkeypatch.setattr(parallel_state, "get_forward_context", lambda: fake_forward_context)
     monkeypatch.setattr(parallel_state, "init_model_parallel_group", fake_init_model_parallel_group)
     monkeypatch.setattr(parallel_state, "init_vllm_model_parallel_group", fake_init_vllm_model_parallel_group)
-    monkeypatch.setattr(parallel_state, "init_dit_group", lambda *_args, **_kwargs: None)
 
-    for name in ("_DP", "_CFG", "_SP", "_PP", "_FS", "_EXPERT_PARALLEL_GROUP_RANKS"):
+    for name in ("_DP", "_CFG", "_SP", "_PP", "_EXPERT_PARALLEL_GROUP_RANKS"):
         monkeypatch.setattr(parallel_state, name, None)
     for name in ("_TP", "_PCP", "_DP", "_EP", "_PP"):
         monkeypatch.setattr(parallel_state.vllm_parallel_state, name, None, raising=False)
@@ -229,9 +228,8 @@ def test_cfg_parallel_keeps_diffusion_dp_without_ep(monkeypatch):
     monkeypatch.setattr(parallel_state.torch.distributed, "new_group", lambda ranks: tuple(ranks))
     monkeypatch.setattr(parallel_state, "get_world_group", lambda: fake_world_group)
     monkeypatch.setattr(parallel_state, "init_model_parallel_group", fake_init_model_parallel_group)
-    monkeypatch.setattr(parallel_state, "init_dit_group", lambda *_args, **_kwargs: None)
 
-    for name in ("_DP", "_CFG", "_SP", "_PP", "_FS", "_EXPERT_PARALLEL_GROUP_RANKS"):
+    for name in ("_DP", "_CFG", "_SP", "_PP", "_EXPERT_PARALLEL_GROUP_RANKS"):
         monkeypatch.setattr(parallel_state, name, None)
     for name in ("_TP", "_PCP", "_DP", "_EP", "_PP"):
         monkeypatch.setattr(parallel_state.vllm_parallel_state, name, None, raising=False)
@@ -259,6 +257,33 @@ def test_cfg_parallel_keeps_diffusion_dp_without_ep(monkeypatch):
     ]
 
 
+@pytest.mark.cpu
+@pytest.mark.core_model
+def test_destroy_model_parallel_clears_vllm_pipeline_group(monkeypatch):
+    """A destroyed diffusion PP group must not block the next initialization."""
+
+    class _DestroyableGroup:
+        def __init__(self) -> None:
+            self.destroy_calls = 0
+
+        def destroy(self) -> None:
+            self.destroy_calls += 1
+
+    pipeline_group = _DestroyableGroup()
+    for name in ("_DP", "_CFG", "_SP"):
+        monkeypatch.setattr(parallel_state, name, None)
+    monkeypatch.setattr(parallel_state, "_PP", pipeline_group)
+    monkeypatch.setattr(parallel_state, "_EXPERT_PARALLEL_GROUP_RANKS", None)
+    for name in ("_DP", "_PCP", "_TP", "_EP"):
+        monkeypatch.setattr(parallel_state.vllm_parallel_state, name, None, raising=False)
+    monkeypatch.setattr(parallel_state.vllm_parallel_state, "_PP", pipeline_group, raising=False)
+
+    parallel_state.destroy_model_parallel()
+
+    assert pipeline_group.destroy_calls == 1
+    assert parallel_state.vllm_parallel_state._PP is None
+
+
 @pytest.mark.cpu
 @pytest.mark.core_model
 def test_non_moe_ep_fails_before_vllm_ep_remap(monkeypatch):
@@ -295,9 +320,8 @@ def test_non_moe_ep_fails_before_vllm_ep_remap(monkeypatch):
     monkeypatch.setattr(parallel_state, "get_world_group", lambda: fake_world_group)
     monkeypatch.setattr(parallel_state, "get_forward_context", lambda: fake_forward_context)
     monkeypatch.setattr(parallel_state, "init_model_parallel_group", fake_init_model_parallel_group)
-    monkeypatch.setattr(parallel_state, "init_dit_group", lambda *_args, **_kwargs: None)
 
-    for name in ("_DP", "_CFG", "_SP", "_PP", "_FS", "_EXPERT_PARALLEL_GROUP_RANKS"):
+    for name in ("_DP", "_CFG", "_SP", "_PP", "_EXPERT_PARALLEL_GROUP_RANKS"):
         monkeypatch.setattr(parallel_state, name, None)
     for name in ("_TP", "_PCP", "_DP", "_EP", "_PP"):
         monkeypatch.setattr(parallel_state.vllm_parallel_state, name, None, raising=False)

tests/diffusion/distributed/test_hsdp.py

@@ -234,7 +234,7 @@ class TestDiffusionParallelConfigHSDP:
 
     def test_hsdp_cannot_use_with_tp(self):
         """Test that HSDP and Tensor Parallelism cannot be used together."""
-        with pytest.raises(ValueError, match="cannot be used with TP or DP"):
+        with pytest.raises(ValueError, match="not compatible with TP"):
             DiffusionParallelConfig(
                 tensor_parallel_size=2,
                 use_hsdp=True,
@@ -243,7 +243,7 @@ class TestDiffusionParallelConfigHSDP:
 
     def test_hsdp_cannot_use_with_dp(self):
         """Test that HSDP and Data Parallelism cannot be used together."""
-        with pytest.raises(ValueError, match="cannot be used with TP or DP"):
+        with pytest.raises(ValueError, match="not compatible with DP"):
             DiffusionParallelConfig(
                 data_parallel_size=2,
                 use_hsdp=True,
@@ -264,158 +264,6 @@ class TestDiffusionParallelConfigHSDP:
         assert config.hsdp_shard_size == 2  # auto: 4 // 2
 
 
-class TestStandaloneHSDPDetection:
-    """Tests for standalone HSDP detection and dit_parallel_size calculation.
-
-    These tests verify the logic used in initialize_model_parallel() to detect
-    standalone HSDP mode and calculate effective parallel sizes.
-
-    Standalone HSDP is when all non-HSDP parallelism dimensions are 1.
-    """
-
-    @staticmethod
-    def compute_standalone_hsdp_params(
-        data_parallel_size: int = 1,
-        cfg_parallel_size: int = 1,
-        sequence_parallel_size: int = 1,
-        pipeline_parallel_size: int = 1,
-        tensor_parallel_size: int = 1,
-        fully_shard_degree: int = 1,
-        hsdp_replicate_size: int = 1,
-    ) -> dict:
-        """Compute standalone HSDP detection parameters.
-
-        This mirrors the logic in initialize_model_parallel().
-        """
-        dit_parallel_size = (
-            data_parallel_size
-            * cfg_parallel_size
-            * sequence_parallel_size
-            * pipeline_parallel_size
-            * tensor_parallel_size
-        )
-
-        # Check for standalone HSDP: all non-HSDP parallelism dimensions are 1
-        is_standalone_hsdp = dit_parallel_size == 1 and fully_shard_degree > 1
-
-        # For standalone HSDP: use (fully_shard_degree * hsdp_replicate_size)
-        if is_standalone_hsdp:
-            effective_dit_parallel_size = fully_shard_degree * hsdp_replicate_size
-        else:
-            effective_dit_parallel_size = dit_parallel_size
-
-        effective_dp_size = (fully_shard_degree * hsdp_replicate_size) if is_standalone_hsdp else data_parallel_size
-
-        return {
-            "original_dit_parallel_size": dit_parallel_size,
-            "is_standalone_hsdp": is_standalone_hsdp,
-            "effective_dit_parallel_size": effective_dit_parallel_size,
-            "effective_dp_size": effective_dp_size,
-        }
-
-    def test_standalone_hsdp_basic(self):
-        """Test basic standalone HSDP detection (shard_size=4, replicate=1)."""
-        result = self.compute_standalone_hsdp_params(
-            fully_shard_degree=4,
-            hsdp_replicate_size=1,
-        )
-        assert result["original_dit_parallel_size"] == 1
-        assert result["is_standalone_hsdp"] is True
-        assert result["effective_dit_parallel_size"] == 4
-        assert result["effective_dp_size"] == 4
-
-    def test_standalone_hsdp_with_replicate(self):
-        """Test standalone HSDP with replication (shard_size=4, replicate=2)."""
-        result = self.compute_standalone_hsdp_params(
-            fully_shard_degree=4,
-            hsdp_replicate_size=2,
-        )
-        assert result["original_dit_parallel_size"] == 1
-        assert result["is_standalone_hsdp"] is True
-        assert result["effective_dit_parallel_size"] == 8  # 4 * 2
-        assert result["effective_dp_size"] == 8
-
-    def test_combined_hsdp_sp_not_standalone(self):
-        """Test HSDP combined with SP is NOT detected as standalone.
-
-        This is a regression test for the bug where the condition
-        `dit_parallel_size == fully_shard_degree` incorrectly matched
-        combined modes like SP=4 + HSDP=4.
-        """
-        result = self.compute_standalone_hsdp_params(
-            sequence_parallel_size=4,
-            fully_shard_degree=4,
-            hsdp_replicate_size=1,
-        )
-        assert result["original_dit_parallel_size"] == 4
-        assert result["is_standalone_hsdp"] is False
-        # Should NOT override dp_size for combined mode
-        assert result["effective_dp_size"] == 1  # original data_parallel_size
-
-    def test_combined_hsdp_cfg_not_standalone(self):
-        """Test HSDP combined with CFG is NOT detected as standalone."""
-        result = self.compute_standalone_hsdp_params(
-            cfg_parallel_size=2,
-            fully_shard_degree=4,
-            hsdp_replicate_size=1,
-        )
-        assert result["original_dit_parallel_size"] == 2
-        assert result["is_standalone_hsdp"] is False
-        assert result["effective_dp_size"] == 1
-
-    def test_combined_hsdp_pp_not_standalone(self):
-        """Test HSDP combined with PP is NOT detected as standalone."""
-        result = self.compute_standalone_hsdp_params(
-            pipeline_parallel_size=2,
-            fully_shard_degree=4,
-            hsdp_replicate_size=1,
-        )
-        assert result["original_dit_parallel_size"] == 2
-        assert result["is_standalone_hsdp"] is False
-        assert result["effective_dp_size"] == 1
-
-    def test_no_hsdp_not_standalone(self):
-        """Test that no HSDP (fully_shard_degree=1) is NOT standalone."""
-        result = self.compute_standalone_hsdp_params(
-            fully_shard_degree=1,
-        )
-        assert result["original_dit_parallel_size"] == 1
-        assert result["is_standalone_hsdp"] is False
-        assert result["effective_dp_size"] == 1
-
-    def test_combined_multiple_parallelism_not_standalone(self):
-        """Test HSDP combined with multiple parallelism is NOT standalone."""
-        result = self.compute_standalone_hsdp_params(
-            sequence_parallel_size=2,
-            cfg_parallel_size=2,
-            fully_shard_degree=4,
-            hsdp_replicate_size=1,
-        )
-        assert result["original_dit_parallel_size"] == 4  # 2 * 2
-        assert result["is_standalone_hsdp"] is False
-        assert result["effective_dp_size"] == 1
-
-    def test_standalone_hsdp_large_shard(self):
-        """Test standalone HSDP with large shard size."""
-        result = self.compute_standalone_hsdp_params(
-            fully_shard_degree=8,
-            hsdp_replicate_size=1,
-        )
-        assert result["is_standalone_hsdp"] is True
-        assert result["effective_dit_parallel_size"] == 8
-        assert result["effective_dp_size"] == 8
-
-    def test_standalone_hsdp_large_replicate(self):
-        """Test standalone HSDP with large replicate size."""
-        result = self.compute_standalone_hsdp_params(
-            fully_shard_degree=4,
-            hsdp_replicate_size=4,
-        )
-        assert result["is_standalone_hsdp"] is True
-        assert result["effective_dit_parallel_size"] == 16  # 4 * 4
-        assert result["effective_dp_size"] == 16
-
-
 class TestHSDPShardConditions:
     """Tests for _hsdp_shard_conditions matching logic."""
 

tests/diffusion/model_loader/test_diffusers_loader.py

@@ -12,7 +12,6 @@ import torch
 import torch.nn as nn
 from huggingface_hub import snapshot_download
 from vllm.config.load import LoadConfig
-from vllm.model_executor.layers.linear import UnquantizedLinearMethod
 
 from vllm_omni.diffusion.config import get_current_diffusion_config, get_current_diffusion_config_or_none
 from vllm_omni.diffusion.data import DiffusionParallelConfig, OmniDiffusionConfig
@@ -310,7 +309,6 @@ def test_dlo_transfers_loader_plan_and_skips_ordinary_weight_loading(monkeypatch
 
     assert loader.load_model(load_device="cpu") is model
     assert not loaded_ordinary_weights
-    assert plan.requires_backend
     assert loader.take_host_weight_plan() is plan
     assert loader.take_host_weight_plan() is None
 
@@ -348,7 +346,7 @@ def test_dlo_plan_loads_component_sources_outside_planned_dit(monkeypatch):
         backing_kind="checkpoint_mmap",
         bindings={
             "transformer.weight": TensorBinding(
-                storage_key="weight",
+                checkpoint_key="weight",
                 file_path="unused",
             )
         },
@@ -376,9 +374,8 @@ def test_dlo_plan_loads_component_sources_outside_planned_dit(monkeypatch):
     assert loader.take_host_weight_plan() is plan
 
 
-def test_dlo_plan_fallback_retains_ordinary_loader_weights_by_default(monkeypatch):
+def test_dlo_plan_fallback_runs_ordinary_loader(monkeypatch):
     import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
-    import vllm_omni.diffusion.model_loader.host_weight_cache as cache_mod
 
     od_config = SimpleNamespace(
         dtype=torch.float32,
@@ -403,202 +400,11 @@ def test_dlo_plan_fallback_retains_ordinary_loader_weights_by_default(monkeypatc
         lambda *_args, **_kwargs: HostWeightPlanResult(None, "not direct-compatible"),
     )
 
-    def build_cache(*_args, **_kwargs):
-        pytest.fail("host weight cache must require explicit selection")
-
-    monkeypatch.setattr(cache_mod, "build_host_weight_cache_plan", build_cache)
-
     assert loader.load_model(load_device="cpu") is model
     assert calls == ["load", "process"]
     assert loader.take_host_weight_plan() is None
 
 
-def test_dlo_host_weight_cache_is_built_after_final_post_load_mutation(monkeypatch, tmp_path):
-    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
-    import vllm_omni.diffusion.model_loader.host_weight_cache as cache_mod
-
-    od_config = SimpleNamespace(
-        dtype=torch.float32,
-        parallel_config=SimpleNamespace(
-            use_hsdp=False,
-            tensor_parallel_size=1,
-            sequence_parallel_size=1,
-            data_parallel_size=2,
-            cfg_parallel_size=1,
-            pipeline_parallel_size=1,
-            ulysses_degree=1,
-            ring_degree=1,
-            allgather_degree=1,
-            ulysses_mode="strict",
-        ),
-        quantization_config=None,
-        enable_distributed_layerwise_offload=True,
-        dlo_use_allgather=False,
-        dlo_use_host_weight_cache=True,
-        dlo_host_weight_cache_dir=str(tmp_path),
-        dlo_host_weight_cache_lock_timeout=1.0,
-        model="unused",
-        revision=None,
-        model_config={},
-    )
-    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
-    model = nn.Module()
-    model.transformer = nn.Linear(2, 2, bias=False)
-    model.transformer.quant_method = UnquantizedLinearMethod()
-    events: list[str] = []
-    runtime_plan = HostWeightPlan(
-        backing_kind="host_weight_cache",
-        bindings={},
-        runtime_layout_key="layout-key",
-    )
-
-    def load_weights(_model):
-        events.append("load")
-        with torch.no_grad():
-            model.transformer.weight.fill_(1)
-
-    def process_weights(_model, _device):
-        events.append("process")
-        with torch.no_grad():
-            model.transformer.weight.add_(1)
-
-    def calibrate(_model):
-        events.append("calibrate")
-        with torch.no_grad():
-            model.transformer.weight.add_(1)
-
-    original_eval = model.eval
-
-    def eval_model():
-        events.append("eval")
-        return original_eval()
-
-    def build_cache(_pipeline, **kwargs):
-        events.append("cache")
-        assert torch.equal(model.transformer.weight, torch.full((2, 2), 3.0))
-        assert kwargs["tensor_parallel_rank"] == 0
-        assert "data_parallel_size" not in kwargs["sequence_parallel_guard"]
-        assert kwargs["quantization_config"] is None
-        return HostWeightPlanResult(runtime_plan)
-
-    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
-    loader.load_weights = load_weights  # type: ignore[method-assign]
-    loader._process_weights_after_loading = process_weights  # type: ignore[method-assign]
-    loader._apply_skip_softmax_calibration = calibrate  # type: ignore[method-assign]
-    model.eval = eval_model  # type: ignore[method-assign]
-    monkeypatch.setattr(
-        loader_mod,
-        "build_checkpoint_mmap_plan",
-        lambda *_args, **_kwargs: HostWeightPlanResult(None, "requires normalization"),
-    )
-    monkeypatch.setattr(cache_mod, "build_host_weight_cache_plan", build_cache)
-
-    assert loader.load_model(load_device="cpu") is model
-    assert events == ["load", "process", "calibrate", "eval", "cache"]
-    assert loader.take_host_weight_plan() is runtime_plan
-
-
-@pytest.mark.parametrize("pin_cpu_memory", [True, False])
-def test_dlo_host_weight_cache_prefers_final_layout_over_checkpoint_plan(monkeypatch, tmp_path, pin_cpu_memory):
-    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
-    import vllm_omni.diffusion.model_loader.host_weight_cache as cache_mod
-
-    od_config = SimpleNamespace(
-        dtype=torch.float32,
-        parallel_config=SimpleNamespace(
-            use_hsdp=False,
-            tensor_parallel_size=1,
-            sequence_parallel_size=1,
-            data_parallel_size=1,
-            cfg_parallel_size=1,
-            pipeline_parallel_size=1,
-        ),
-        quantization_config=None,
-        enable_distributed_layerwise_offload=True,
-        dlo_use_allgather=False,
-        dlo_use_host_weight_cache=True,
-        pin_cpu_memory=pin_cpu_memory,
-        dlo_host_weight_cache_dir=str(tmp_path),
-        dlo_host_weight_cache_pin_limit_gib=0.0,
-        model="unused",
-    )
-    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
-    model = nn.Module()
-    model.transformer = nn.Linear(2, 2, bias=False)
-    events: list[str] = []
-    runtime_plan = HostWeightPlan(
-        backing_kind="host_weight_cache",
-        bindings={},
-        runtime_layout_key="layout-key",
-    )
-
-    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
-    loader.load_weights = lambda _model: events.append("load")  # type: ignore[method-assign]
-    loader._process_weights_after_loading = lambda *_args: events.append("process")  # type: ignore[method-assign]
-    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
-
-    def unexpected_checkpoint_plan(*_args, **_kwargs):
-        pytest.fail("selected host weight cache mode must not select the raw checkpoint mmap plan")
-
-    def build_cache(_pipeline, **_kwargs):
-        events.append("cache")
-        return HostWeightPlanResult(runtime_plan)
-
-    monkeypatch.setattr(loader_mod, "build_checkpoint_mmap_plan", unexpected_checkpoint_plan)
-    monkeypatch.setattr(cache_mod, "build_host_weight_cache_plan", build_cache)
-
-    assert loader.load_model(load_device="cpu") is model
-    assert events == ["load", "process", "cache"]
-    assert loader.take_host_weight_plan() is runtime_plan
-
-
-def test_dlo_host_weight_cache_failure_retains_ordinary_weights(monkeypatch, tmp_path):
-    import vllm_omni.diffusion.model_loader.diffusers_loader as loader_mod
-    import vllm_omni.diffusion.model_loader.host_weight_cache as cache_mod
-
-    od_config = SimpleNamespace(
-        dtype=torch.float32,
-        parallel_config=SimpleNamespace(
-            use_hsdp=False,
-            tensor_parallel_size=1,
-            sequence_parallel_size=1,
-            data_parallel_size=2,
-            cfg_parallel_size=1,
-            pipeline_parallel_size=1,
-        ),
-        quantization_config=None,
-        enable_distributed_layerwise_offload=True,
-        dlo_use_allgather=False,
-        dlo_use_host_weight_cache=True,
-        dlo_host_weight_cache_dir=str(tmp_path),
-        model="unused",
-    )
-    loader = DiffusersPipelineLoader(LoadConfig(), od_config)
-    model = nn.Module()
-    model.transformer = nn.Linear(2, 2, bias=False)
-    model.transformer.quant_method = object()
-    loader._init_from_load_format = lambda *_args, **_kwargs: model  # type: ignore[method-assign]
-    loader.load_weights = lambda _model: None  # type: ignore[method-assign]
-    loader._process_weights_after_loading = lambda *_args: None  # type: ignore[method-assign]
-    loader._apply_skip_softmax_calibration = lambda _model: None  # type: ignore[method-assign]
-    monkeypatch.setattr(
-        loader_mod,
-        "build_checkpoint_mmap_plan",
-        lambda *_args, **_kwargs: HostWeightPlanResult(None, "requires normalization"),
-    )
-
-    def build_cache(_pipeline, **kwargs):
-        assert kwargs["quantization_config"] == "module_quant_method"
-        return HostWeightPlanResult(None, "writer timed out", "lock_timeout")
-
-    monkeypatch.setattr(cache_mod, "build_host_weight_cache_plan", build_cache)
-
-    original_weight = model.transformer.weight.detach().clone()
-    assert loader.load_model(load_device="cpu") is model
-    assert torch.equal(model.transformer.weight, original_weight)
-    assert loader.take_host_weight_plan() is None
-
-
 def test_dlo_allgather_online_fp8_uses_ordinary_loader(monkeypatch):
     from vllm.model_executor.layers.quantization.online.fp8 import (
         Fp8PerTensorOnlineLinearMethod,

tests/diffusion/model_loader/test_fp8_cache.py

@@ -0,0 +1,153 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Unit tests for the Phase-I normalized online-FP8 cache contract."""
+
+import json
+from types import SimpleNamespace
+
+import pytest
+import torch
+from safetensors.torch import load_file, save_file
+from torch import nn
+
+from vllm_omni.diffusion.model_loader.fp8_cache import (
+    CACHE_COMPLETE_NAME,
+    CACHE_COMPONENT_SUBFOLDER,
+    CACHE_MANIFEST_NAME,
+    _source_fingerprint,
+    _transformer_quant_config,
+    build_normalized_fp8_cache,
+    is_online_fp8_config,
+    load_fp8_cache_manifest,
+)
+
+pytestmark = [pytest.mark.diffusion, pytest.mark.cpu, pytest.mark.core_model]
+
+
+def _write_valid_cache(tmp_path):
+    source = tmp_path / "source.safetensors"
+    save_file({"weight": torch.ones(2, 2)}, str(source))
+
+    cache = tmp_path / "cache"
+    component = cache / CACHE_COMPONENT_SUBFOLDER
+    component.mkdir(parents=True)
+    shard = component / "part-00000.safetensors"
+    save_file({"weight": torch.ones(2, 2, dtype=torch.float8_e4m3fn)}, str(shard))
+    (component / "model.safetensors.index.json").write_text(
+        json.dumps(
+            {
+                "metadata": {"total_size": 4},
+                "weight_map": {"weight": shard.name},
+            }
+        )
+    )
+
+    manifest = {
+        "schema_version": 1,
+        "cache_kind": "normalized_fp8_runtime",
+        "component": "transformer",
+        "component_subfolder": CACHE_COMPONENT_SUBFOLDER,
+        "source_prefix": "transformer.",
+        "quantization": {"method": "fp8", "serialized": True, "activation_scheme": "dynamic"},
+        "tensor_count": 1,
+        "source_files": [str(source)],
+        "source_fingerprint": _source_fingerprint({str(source)}),
+    }
+    cache.mkdir(exist_ok=True)
+    (cache / CACHE_MANIFEST_NAME).write_text(json.dumps(manifest))
+    (cache / CACHE_COMPLETE_NAME).write_text("phase1\n")
+    return cache, source, manifest
+
+
+def test_online_and_serialized_fp8_configs_are_classified():
+    from vllm.model_executor.layers.quantization.fp8 import Fp8Config
+
+    assert is_online_fp8_config(Fp8Config())
+    assert not is_online_fp8_config(Fp8Config(is_checkpoint_fp8_serialized=True))
+
+    cache_config = _transformer_quant_config()
+    assert not is_online_fp8_config(cache_config)
+    assert cache_config.resolve("transformer").is_checkpoint_fp8_serialized
+    assert cache_config.resolve("text_encoder") is None
+
+
+def test_load_fp8_cache_manifest_validates_artifact_and_sources(tmp_path):
+    cache, source, expected = _write_valid_cache(tmp_path)
+
+    assert load_fp8_cache_manifest(cache) == expected
+
+    source.write_bytes(source.read_bytes() + b"changed")
+    assert load_fp8_cache_manifest(cache) is None
+
+
+def test_load_fp8_cache_manifest_rejects_wrong_serialization_contract(tmp_path):
+    cache, _, manifest = _write_valid_cache(tmp_path)
+
+    manifest["quantization"]["serialized"] = False
+    (cache / CACHE_MANIFEST_NAME).write_text(json.dumps(manifest))
+
+    assert load_fp8_cache_manifest(cache) is None
+
+
+def test_load_fp8_cache_manifest_rejects_path_traversal(tmp_path):
+    cache, _, manifest = _write_valid_cache(tmp_path)
+    index_path = cache / CACHE_COMPONENT_SUBFOLDER / "model.safetensors.index.json"
+    index_path.write_text(json.dumps({"weight_map": {"weight": "../outside.safetensors"}}))
+
+    assert load_fp8_cache_manifest(cache) is None
+
+
+def test_load_fp8_cache_manifest_rejects_non_object_json(tmp_path):
+    cache, _, _ = _write_valid_cache(tmp_path)
+    (cache / CACHE_MANIFEST_NAME).write_text("[]")
+
+    assert load_fp8_cache_manifest(cache) is None
+
+
+def test_build_normalized_fp8_cache_serializes_weight_and_scale(tmp_path):
+    from vllm.model_executor.layers.quantization.online.fp8 import (
+        Fp8PerTensorOnlineLinearMethod,
+    )
+
+    source_dir = tmp_path / "source"
+    source_dir.mkdir()
+    source_file = source_dir / "model.safetensors"
+    save_file({"block.weight": torch.ones((3, 2), dtype=torch.bfloat16)}, str(source_file))
+
+    model = nn.Module()
+    model._dit_modules = ("transformer",)
+    model.transformer = nn.Module()
+    model.transformer.block = nn.Module()
+    model.transformer.block.weight = nn.Parameter(torch.ones((2, 3), dtype=torch.float8_e4m3fn), requires_grad=False)
+    model.transformer.block.weight_scale = nn.Parameter(torch.tensor([0.5]), requires_grad=False)
+    model.transformer.block.quant_method = object.__new__(Fp8PerTensorOnlineLinearMethod)
+
+    source = SimpleNamespace(
+        model_or_path=str(source_dir),
+        subfolder=None,
+        revision=None,
+        prefix="transformer.",
+    )
+    cache_dir = tmp_path / "cache"
+
+    manifest = build_normalized_fp8_cache(
+        model,
+        sources=(source,),
+        cache_dir=cache_dir,
+    )
+
+    assert manifest["cache_kind"] == "normalized_fp8_runtime"
+    tensors = load_file(str(cache_dir / CACHE_COMPONENT_SUBFOLDER / "part-00000.safetensors"))
+    scale_tensors = load_file(str(cache_dir / CACHE_COMPONENT_SUBFOLDER / "part-00001.safetensors"))
+    assert tensors["block.weight"].dtype == torch.float8_e4m3fn
+    assert tuple(tensors["block.weight"].shape) == (3, 2)
+    assert torch.equal(scale_tensors["block.weight_scale"], torch.tensor([0.5]))
+
+
+@pytest.mark.parametrize("missing", [CACHE_COMPLETE_NAME, CACHE_MANIFEST_NAME])
+def test_load_fp8_cache_manifest_requires_completion_marker(tmp_path, missing):
+    cache, _, _ = _write_valid_cache(tmp_path)
+    (cache / missing).unlink()
+
+    assert load_fp8_cache_manifest(cache) is None

tests/diffusion/model_loader/test_host_weight_cache.py

@@ -1,260 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import fcntl
-import multiprocessing as mp
-import shutil
-from pathlib import Path
-
-import pytest
-import torch
-from torch import nn
-
-from vllm_omni.diffusion.model_loader.host_weight_cache import (
-    build_host_weight_cache_plan,
-    default_host_weight_cache_root,
-)
-
-pytestmark = [pytest.mark.diffusion, pytest.mark.cpu, pytest.mark.core_model]
-
-_SPAWN_TIMEOUT_SECONDS = 120
-
-
-class _LoaderMarker:
-    pass
-
-
-class _Transformer(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.proj = nn.Linear(3, 2)
-        self.register_buffer("persistent", torch.arange(2, dtype=torch.float32))
-        self.register_buffer("temporary", torch.ones(1), persistent=False)
-        with torch.no_grad():
-            self.proj.weight.copy_(torch.arange(6, dtype=torch.float32).reshape(2, 3))
-            self.proj.bias.copy_(torch.tensor([7.0, 8.0]))
-
-
-class _Pipeline(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.transformer = _Transformer()
-
-
-def test_default_root_is_not_split_by_replica_vllm_cache(monkeypatch):
-    monkeypatch.setenv("VLLM_CACHE_ROOT", "/tmp/replica-specific-vllm-cache")
-
-    assert default_host_weight_cache_root().endswith("/.cache/vllm-omni/dlo-host-weights")
-
-
-def _build(pipeline: nn.Module, cache_root: Path, **overrides):
-    kwargs = {
-        "dit_modules": (("transformer", pipeline.transformer),),
-        "loader_type": _LoaderMarker,
-        "cache_root": cache_root,
-        "lock_timeout_seconds": 2.0,
-        "max_shard_bytes": 32,
-        "model_identity": "test/model",
-        "revision": "revision",
-        "runtime_dtype": torch.float32,
-        "load_format": "default",
-        "loader_inputs": {"model_config": {"hidden_size": 3}},
-        "tensor_parallel_size": 1,
-        "tensor_parallel_rank": 0,
-        "sequence_parallel_guard": {"sequence_parallel_size": 1, "backend": "none"},
-        "use_hsdp": False,
-        "enable_expert_parallel": False,
-        "quantization_config": None,
-        "cfg_parallel_size": 1,
-        "pipeline_parallel_size": 1,
-    }
-    kwargs.update(overrides)
-    return build_host_weight_cache_plan(pipeline, **kwargs)
-
-
-def _multiprocess_builder(cache_root: str, queue) -> None:
-    result = _build(_Pipeline(), Path(cache_root))
-    queue.put(
-        (
-            result.fallback_code,
-            result.plan.runtime_layout_key if result.plan is not None else None,
-            sorted(binding.file_path for binding in result.plan.bindings.values()) if result.plan is not None else [],
-        )
-    )
-
-
-def test_equivalent_process_layouts_reuse_one_entry(tmp_path):
-    first = _build(_Pipeline(), tmp_path)
-    second = _build(_Pipeline(), tmp_path)
-
-    assert first.plan is not None
-    assert second.plan is not None
-    assert first.plan.runtime_layout_key == second.plan.runtime_layout_key
-    assert not first.plan.requires_backend
-    assert {binding.file_path for binding in first.plan.bindings.values()} == {
-        binding.file_path for binding in second.plan.bindings.values()
-    }
-    assert set(first.plan.bindings) == {
-        "transformer.persistent",
-        "transformer.proj.bias",
-        "transformer.proj.weight",
-    }
-
-
-def test_equivalent_local_model_paths_reuse_one_entry(tmp_path, monkeypatch):
-    model_dir = tmp_path / "model"
-    model_dir.mkdir()
-    model_link = tmp_path / "model-link"
-    model_link.symlink_to(model_dir, target_is_directory=True)
-    cache_root = tmp_path / "cache"
-    monkeypatch.setenv("HOME", str(tmp_path))
-    monkeypatch.chdir(tmp_path)
-
-    identities = (str(model_dir), "~/model", "model", str(model_link))
-    results = [_build(_Pipeline(), cache_root, model_identity=identity) for identity in identities]
-
-    assert all(result.plan is not None for result in results)
-    assert len({result.plan.runtime_layout_key for result in results if result.plan is not None}) == 1
-    assert len(list((cache_root / "v1").iterdir())) == 1
-
-
-def test_unstable_loader_identity_fails_closed(tmp_path):
-    result = _build(_Pipeline(), tmp_path, loader_inputs={"opaque": object()})
-
-    assert result.plan is None
-    assert result.fallback_code == "unstable_identity"
-    assert result.fallback_reason == "host weight cache identity does not support values of type builtins.object"
-    assert list(tmp_path.rglob("*.safetensors")) == []
-
-
-def test_tp_coordinate_and_sp_implementation_guard_split_entries(tmp_path):
-    pipeline = _Pipeline()
-    tp0 = _build(pipeline, tmp_path, tensor_parallel_size=2, tensor_parallel_rank=0)
-    tp1 = _build(pipeline, tmp_path, tensor_parallel_size=2, tensor_parallel_rank=1)
-    sp2 = _build(
-        pipeline,
-        tmp_path,
-        sequence_parallel_guard={"sequence_parallel_size": 2, "backend": "ulysses"},
-    )
-
-    assert tp0.plan is not None and tp1.plan is not None and sp2.plan is not None
-    assert len({tp0.plan.runtime_layout_key, tp1.plan.runtime_layout_key, sp2.plan.runtime_layout_key}) == 3
-
-
-def test_final_runtime_content_is_authoritative(tmp_path):
-    first_pipeline = _Pipeline()
-    second_pipeline = _Pipeline()
-    with torch.no_grad():
-        second_pipeline.transformer.proj.weight[0, 0] += 1
-
-    first = _build(first_pipeline, tmp_path)
-    second = _build(second_pipeline, tmp_path)
-
-    assert first.plan is not None and second.plan is not None
-    assert first.plan.runtime_layout_key != second.plan.runtime_layout_key
-
-
-def test_expert_parallel_ownership_fails_closed(tmp_path):
-    result = _build(_Pipeline(), tmp_path, enable_expert_parallel=True)
-
-    assert result.plan is None
-    assert result.fallback_code == "unsupported_expert_parallel"
-    assert list(tmp_path.rglob("*.safetensors")) == []
-
-
-def test_pipeline_parallel_ownership_fails_closed(tmp_path):
-    result = _build(_Pipeline(), tmp_path, pipeline_parallel_size=2)
-
-    assert result.plan is None
-    assert result.fallback_code == "unsupported_pp"
-    assert list(tmp_path.rglob("*.safetensors")) == []
-
-
-def test_corrupt_published_entry_is_rebuilt_under_the_key_lock(tmp_path):
-    first = _build(_Pipeline(), tmp_path)
-    assert first.plan is not None
-    shard = Path(next(iter(first.plan.bindings.values())).file_path)
-    old_inode = shard.stat().st_ino
-    with shard.open("r+b") as handle:
-        handle.seek(-1, 2)
-        byte = handle.read(1)
-        handle.seek(-1, 2)
-        handle.write(bytes([byte[0] ^ 0xFF]))
-
-    second = _build(_Pipeline(), tmp_path)
-
-    assert second.plan is not None
-    assert second.plan.runtime_layout_key == first.plan.runtime_layout_key
-    assert Path(next(iter(second.plan.bindings.values())).file_path).stat().st_ino != old_inode
-
-
-def test_lock_timeout_keeps_the_ordinary_tensors(tmp_path):
-    initial = _build(_Pipeline(), tmp_path)
-    assert initial.plan is not None
-    cache_key = initial.plan.runtime_layout_key
-    entry_dir = Path(next(iter(initial.plan.bindings.values())).file_path).parent
-    shutil.rmtree(entry_dir)
-    lock_path = tmp_path / ".locks" / f"{cache_key}.lock"
-
-    with lock_path.open("a+b") as lock_handle:
-        fcntl.flock(lock_handle.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
-        result = _build(_Pipeline(), tmp_path, lock_timeout_seconds=0.01)
-
-    assert result.plan is None
-    assert result.fallback_code == "lock_timeout"
-
-
-def test_next_writer_removes_stale_temp_after_process_death(tmp_path):
-    initial = _build(_Pipeline(), tmp_path)
-    assert initial.plan is not None
-    cache_key = initial.plan.runtime_layout_key
-    entry_dir = Path(next(iter(initial.plan.bindings.values())).file_path).parent
-    shutil.rmtree(entry_dir)
-    stale_temp = tmp_path / ".tmp" / f"{cache_key}.stale-writer"
-    stale_temp.mkdir(parents=True)
-    (stale_temp / "partial").write_bytes(b"partial")
-
-    result = _build(_Pipeline(), tmp_path)
-
-    assert result.plan is not None
-    assert not stale_temp.exists()
-
-
-@pytest.mark.parametrize("case", ["alias", "external_alias", "noncontiguous"])
-def test_unsupported_tensor_layouts_fail_closed(tmp_path, case):
-    pipeline = _Pipeline()
-    if case == "alias":
-        pipeline.transformer.tied = pipeline.transformer.proj.weight
-        expected_code = "unsupported_alias"
-    elif case == "external_alias":
-        pipeline.encoder = nn.Module()
-        pipeline.encoder.shared = pipeline.transformer.proj.weight
-        expected_code = "unsupported_alias"
-    else:
-        pipeline.transformer.proj.weight = nn.Parameter(torch.arange(6, dtype=torch.float32).reshape(3, 2).t())
-        expected_code = "unsupported_tensor"
-
-    result = _build(pipeline, tmp_path)
-
-    assert result.plan is None
-    assert result.fallback_code == expected_code
-    assert list(tmp_path.rglob("*.safetensors")) == []
-
-
-def test_concurrent_processes_publish_one_immutable_entry(tmp_path):
-    context = mp.get_context("spawn")
-    queue = context.Queue()
-    processes = [context.Process(target=_multiprocess_builder, args=(str(tmp_path), queue)) for _ in range(2)]
-    for process in processes:
-        process.start()
-    # Spawn workers import the full vLLM stack before entering the target;
-    # loaded CI hosts can spend more than 30 seconds in that startup path.
-    results = [queue.get(timeout=_SPAWN_TIMEOUT_SECONDS) for _ in processes]
-    for process in processes:
-        process.join(timeout=_SPAWN_TIMEOUT_SECONDS)
-
-    assert all(process.exitcode == 0 for process in processes)
-    assert all(code is None for code, _, _ in results)
-    assert len({key for _, key, _ in results}) == 1
-    assert len({tuple(paths) for _, _, paths in results}) == 1
-    assert len(list((tmp_path / "v1").iterdir())) == 1

tests/diffusion/offloader/test_cuda_host_registration.py

@@ -1,346 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-import ctypes
-from types import SimpleNamespace
-
-import pytest
-import torch
-
-import vllm_omni.diffusion.offloader.cuda_host_registration as registration_module
-from vllm_omni.diffusion.offloader.cuda_host_registration import (
-    CudaHostRegistration,
-    CudaHostRegistrationBudgetError,
-    CudaHostRegistrationCleanupError,
-    CudaHostRegistrationError,
-    _coalesce_ranges,
-)
-from vllm_omni.diffusion.offloader.host_registration import (
-    HostRegistrationError,
-    register_host_mappings,
-)
-
-pytestmark = [pytest.mark.diffusion, pytest.mark.cpu, pytest.mark.core_model]
-
-
-class _FakeStorage:
-    def __init__(self, pointer: int, size: int) -> None:
-        self._pointer = pointer
-        self._size = size
-
-    def data_ptr(self) -> int:
-        return self._pointer
-
-    def nbytes(self) -> int:
-        return self._size
-
-
-class _FakeTensor:
-    device = SimpleNamespace(type="cpu")
-
-    def __init__(self, pointer: int, size: int, *, pinned: bool = True) -> None:
-        self._storage = _FakeStorage(pointer, size)
-        self._pinned = pinned
-
-    def untyped_storage(self) -> _FakeStorage:
-        return self._storage
-
-    def numel(self) -> int:
-        return self._storage.nbytes()
-
-    def is_pinned(self) -> bool:
-        return self._pinned
-
-
-class _FakeRuntime:
-    def __init__(
-        self,
-        register_results: list[int | Exception],
-        unregister_results: list[int | Exception] | None = None,
-        *,
-        read_only_registration_supported: bool = True,
-        attribute_result: int = 0,
-    ) -> None:
-        self._register_results = iter(register_results)
-        self._unregister_results = iter(unregister_results or [])
-        self._read_only_registration_supported = read_only_registration_supported
-        self._attribute_result = attribute_result
-        self.attribute_queries: list[tuple[int, int]] = []
-        self.registered: list[tuple[int, int, int]] = []
-        self.unregistered: list[int] = []
-
-    def cudaDeviceGetAttribute(self, value, attribute: int, device: int) -> int:
-        self.attribute_queries.append((attribute, device))
-        ctypes.cast(value, ctypes.POINTER(ctypes.c_int)).contents.value = int(self._read_only_registration_supported)
-        return self._attribute_result
-
-    def cudaHostRegister(self, pointer: int, size: int, flags: int) -> int:
-        self.registered.append((pointer, size, flags))
-        result = next(self._register_results)
-        if isinstance(result, Exception):
-            raise result
-        return result
-
-    def cudaHostUnregister(self, pointer: int) -> int:
-        self.unregistered.append(pointer)
-        result = next(self._unregister_results, 0)
-        if isinstance(result, Exception):
-            raise result
-        return result
-
-    @staticmethod
-    def cudaGetErrorString(error: int) -> str:
-        return f"error-{error}"
-
-
-@pytest.fixture(autouse=True)
-def _current_cuda_device(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.accelerator, "current_device_index", lambda: 0)
-
-
-def test_platform_factory_reports_unsupported_registration() -> None:
-    with pytest.raises(HostRegistrationError, match="not supported on cpu"):
-        register_host_mappings({}, device=torch.device("cpu"), max_bytes=4096)
-
-
-def test_platform_factory_dispatches_cuda(monkeypatch: pytest.MonkeyPatch) -> None:
-    sentinel = object()
-    sources = {"weights": []}
-
-    def create(actual_sources, *, max_bytes):
-        assert actual_sources is sources
-        assert max_bytes == 4096
-        return sentinel
-
-    monkeypatch.setattr(CudaHostRegistration, "create", staticmethod(create))
-
-    assert register_host_mappings(sources, device=torch.device("cuda"), max_bytes=4096) is sentinel
-
-
-def test_platform_factory_allows_registration_without_additional_budget(monkeypatch: pytest.MonkeyPatch) -> None:
-    sentinel = object()
-    sources = {"weights": []}
-
-    def create(actual_sources, *, max_bytes):
-        assert actual_sources is sources
-        assert max_bytes is None
-        return sentinel
-
-    monkeypatch.setattr(CudaHostRegistration, "create", staticmethod(create))
-
-    assert register_host_mappings(sources, device=torch.device("cuda"), max_bytes=None) is sentinel
-
-
-def test_coalesce_ranges_aligns_and_merges_overlapping_pages() -> None:
-    assert _coalesce_ranges(
-        [(0x1003, 4096), (0x2800, 1024), (0x9001, 1)],
-        page_size=4096,
-    ) == (
-        registration_module._AddressRange(0x1000, 0x3000),
-        registration_module._AddressRange(0x9000, 0xA000),
-    )
-
-
-def test_registration_rejects_over_budget_before_calling_cuda(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0])
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-
-    with pytest.raises(CudaHostRegistrationBudgetError, match="exceeding"):
-        CudaHostRegistration.create(
-            {"weights": [_FakeTensor(0x1003, 4096)]},  # type: ignore[list-item]
-            max_bytes=4096,
-        )
-
-    assert runtime.registered == []
-
-
-def test_registration_without_additional_budget_registers_complete_mapping(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0])
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-
-    registration = CudaHostRegistration.create(
-        {"weights": [_FakeTensor(0x1003, 4096)]},  # type: ignore[list-item]
-        max_bytes=None,
-    )
-
-    assert registration.total_bytes == 8192
-    assert len(runtime.registered) == 1
-    assert registration.close() == []
-
-
-def test_registration_rejects_unsupported_read_only_mapping(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0], read_only_registration_supported=False)
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-
-    with pytest.raises(CudaHostRegistrationError, match="does not support read-only host registration"):
-        CudaHostRegistration.create(
-            {"weights": [_FakeTensor(0x1003, 1)]},  # type: ignore[list-item]
-            max_bytes=4096,
-        )
-
-    assert runtime.attribute_queries == [
-        (registration_module._CUDA_DEVICE_ATTRIBUTE_HOST_REGISTER_READ_ONLY_SUPPORTED, 0)
-    ]
-    assert runtime.registered == []
-
-
-def test_registration_consumes_capability_query_error(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0], attribute_result=7)
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-    consumed_errors: list[int] = []
-    monkeypatch.setattr(registration_module, "_consume_last_cuda_error", consumed_errors.append)
-
-    with pytest.raises(CudaHostRegistrationError, match="error-7"):
-        CudaHostRegistration.create(
-            {"weights": [_FakeTensor(0x1003, 1)]},  # type: ignore[list-item]
-            max_bytes=4096,
-        )
-
-    assert consumed_errors == [7]
-    assert runtime.registered == []
-
-
-def test_registration_rolls_back_partial_success(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0, 7])
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-    consumed_errors: list[int] = []
-    monkeypatch.setattr(registration_module, "_consume_last_cuda_error", consumed_errors.append)
-
-    with pytest.raises(CudaHostRegistrationError, match="error-7"):
-        CudaHostRegistration.create(
-            {
-                "first": [_FakeTensor(0x1003, 1)],  # type: ignore[list-item]
-                "second": [_FakeTensor(0x9003, 1)],  # type: ignore[list-item]
-            },
-            max_bytes=8192,
-        )
-
-    assert runtime.unregistered == [0x1000]
-    assert consumed_errors == [7]
-
-
-def test_registration_wraps_runtime_exception_and_rolls_back(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0, RuntimeError("driver rejected mapping")])
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-
-    with pytest.raises(CudaHostRegistrationError, match="driver rejected mapping"):
-        CudaHostRegistration.create(
-            {
-                "first": [_FakeTensor(0x1003, 1)],  # type: ignore[list-item]
-                "second": [_FakeTensor(0x9003, 1)],  # type: ignore[list-item]
-            },
-            max_bytes=8192,
-        )
-
-    assert runtime.unregistered == [0x1000]
-
-
-def test_registration_fails_closed_when_rollback_fails(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0, 7], unregister_results=[9])
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-    consumed_errors: list[int] = []
-    monkeypatch.setattr(registration_module, "_consume_last_cuda_error", consumed_errors.append)
-
-    with pytest.raises(CudaHostRegistrationCleanupError, match="rollback errors"):
-        CudaHostRegistration.create(
-            {
-                "first": [_FakeTensor(0x1003, 1)],  # type: ignore[list-item]
-                "second": [_FakeTensor(0x9003, 1)],  # type: ignore[list-item]
-            },
-            max_bytes=8192,
-        )
-
-    assert consumed_errors == [7, 9]
-
-
-@pytest.mark.parametrize("pending_error", [0, 801])
-def test_consume_last_cuda_error_accepts_cleared_or_matching_state(
-    monkeypatch: pytest.MonkeyPatch,
-    pending_error: int,
-) -> None:
-    class _GetLastError:
-        def __call__(self) -> int:
-            return pending_error
-
-    runtime = SimpleNamespace(cudaGetLastError=_GetLastError())
-    monkeypatch.setattr(registration_module.ctypes, "CDLL", lambda _name: runtime)
-
-    registration_module._consume_last_cuda_error(801)
-
-
-def test_consume_last_cuda_error_rejects_unrelated_pending_error(monkeypatch: pytest.MonkeyPatch) -> None:
-    class _GetLastError:
-        def __call__(self) -> int:
-            return 700
-
-    runtime = SimpleNamespace(cudaGetLastError=_GetLastError())
-    monkeypatch.setattr(registration_module.ctypes, "CDLL", lambda _name: runtime)
-
-    with pytest.raises(CudaHostRegistrationCleanupError, match="cudaGetLastError reported 700"):
-        registration_module._consume_last_cuda_error(801)
-
-
-def test_registration_closes_all_coalesced_ranges(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0, 0])
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-    registration = CudaHostRegistration.create(
-        {
-            "first": [_FakeTensor(0x1003, 4096), _FakeTensor(0x2800, 1024)],  # type: ignore[list-item]
-            "second": [_FakeTensor(0x9003, 1)],  # type: ignore[list-item]
-        },
-        max_bytes=12288,
-    )
-
-    assert registration.total_bytes == 12288
-    assert registration.region_count == 2
-    assert registration.close() == []
-    assert runtime.unregistered == [0x9000, 0x1000]
-    assert registration.close() == []
-
-
-def test_registration_keeps_adjacent_file_mappings_separate(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0, 0])
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-
-    registration = CudaHostRegistration.create(
-        {
-            "first.safetensors": [_FakeTensor(0x1000, 4096)],  # type: ignore[list-item]
-            "second.safetensors": [_FakeTensor(0x2000, 4096)],  # type: ignore[list-item]
-        },
-        max_bytes=8192,
-    )
-
-    assert registration.region_count == 2
-    assert runtime.registered == [
-        (0x1000, 4096, registration_module._CUDA_HOST_REGISTER_READ_ONLY),
-        (0x2000, 4096, registration_module._CUDA_HOST_REGISTER_READ_ONLY),
-    ]
-    assert registration.close() == []
-
-
-def test_registration_close_retries_failed_ranges(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(registration_module.torch.cuda, "is_available", lambda: True)
-    runtime = _FakeRuntime([0], unregister_results=[9, 0])
-    monkeypatch.setattr(registration_module.torch.cuda, "cudart", lambda: runtime)
-    consumed_errors: list[int] = []
-    monkeypatch.setattr(registration_module, "_consume_last_cuda_error", consumed_errors.append)
-    registration = CudaHostRegistration.create(
-        {"weights": [_FakeTensor(0x1003, 1)]},  # type: ignore[list-item]
-        max_bytes=4096,
-    )
-
-    assert registration.close() == ["cudaHostUnregister(0x1000) failed: error-9"]
-    assert registration.close() == []
-    assert runtime.unregistered == [0x1000, 0x1000]
-    assert consumed_errors == [9]

tests/diffusion/offloader/test_distributed_layerwise_backend.py

@@ -18,7 +18,6 @@ from torch import nn
 from torch.distributed.tensor import DeviceMesh, DTensor, Replicate
 
 import vllm_omni.diffusion.offloader.distributed_layerwise_backend as dist_backend_module
-from vllm_omni.diffusion.model_loader.host_weight_cache_utils import file_digest
 from vllm_omni.diffusion.model_loader.host_weight_plan import (
     HostWeightPlan,
     TensorBinding,
@@ -547,31 +546,6 @@ class TestDistributedLayerwiseOffloadHook:
         assert staging[0][torch.float32].numel() == 12
         assert staging[1][torch.float32].numel() == 12
 
-    def test_registered_mmap_copies_directly_without_staging(self, patched_offload_runtime):
-        current_block = nn.Linear(2, 2, bias=False)
-        next_block = nn.Linear(2, 2, bias=False)
-        expected = torch.arange(4, dtype=torch.float32).view(2, 2)
-        next_block.weight.data.copy_(expected)
-        hook = DistributedLayerwiseOffloadHook(
-            next_block=next_block,
-            device=torch.device("cpu"),
-            dp_group=None,
-            dp_size=1,
-            rank=0,
-            pin_memory=False,
-            rank_local_mmap=True,
-        )
-        hook.initialize_hook(current_block)
-        hook.registered_mmap = True
-
-        def fail_staging(_slot):
-            raise AssertionError("registered mmap must bypass host staging")
-
-        hook._stage_mmap_sources = fail_staging  # type: ignore[method-assign]
-        hook.prefetch_layer(slot=0, non_blocking=True)
-
-        assert torch.equal(next_block.weight, expected)
-
 
 class TestPinnedResidentLayerGroup:
     def test_load_offload_reuses_pinned_master_weights(self, patched_offload_runtime):
@@ -662,24 +636,6 @@ class TestPinnedResidentLayerGroup:
         group.offload()
         assert all(block.weight.numel() == 0 for block in blocks)
 
-    def test_registered_mmap_resident_layers_bypass_staging(self, patched_offload_runtime):
-        block = nn.Linear(2, 2, bias=False)
-        expected = torch.arange(4, dtype=torch.float32).view(2, 2)
-        block.weight.data.copy_(expected)
-        group = PinnedResidentLayerGroup(
-            [block],
-            device=torch.device("cpu"),
-            copy_stream=DummyStream(),
-            pin_memory=False,
-            rank_local_mmap=True,
-        )
-        group.registered_mmap = True
-        group._cpu_staging_buffers.clear()
-
-        group.load()
-
-        assert torch.equal(block.weight, expected)
-
     def test_shared_allgather_output_is_narrowed_to_current_block(
         self, dist_group, patched_offload_runtime, monkeypatch
     ):
@@ -809,140 +765,6 @@ class _MmapPostLoadPipeline(nn.Module):
 
 
 class TestMmapWeightLoading:
-    def test_host_weight_cache_maps_final_weights_without_rerunning_post_load(
-        self,
-        tmp_path,
-        patched_offload_runtime,
-    ):
-        class Transformer(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.proj = nn.Linear(2, 2, bias=False)
-                self.post_load_calls = 0
-
-            def post_load_weights(self):
-                self.post_load_calls += 1
-
-        class Pipeline(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.transformer = Transformer()
-
-        pipeline = Pipeline()
-        old_parameter = pipeline.transformer.proj.weight
-        old_parameter.runtime_marker = "preserved"
-        weight_file = tmp_path / "runtime.safetensors"
-        save_file({"transformer.proj.weight": torch.full((2, 2), 9.0)}, str(weight_file))
-        plan = HostWeightPlan(
-            backing_kind="host_weight_cache",
-            bindings={
-                "transformer.proj.weight": TensorBinding(
-                    storage_key="transformer.proj.weight",
-                    file_path=str(weight_file),
-                )
-            },
-            runtime_layout_key="runtime-layout",
-            expected_file_digests={str(weight_file): file_digest(weight_file)},
-        )
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                dlo_use_allgather=False,
-            ),
-            torch.device("cpu"),
-            host_weight_plan=plan,
-        )
-        modules = SimpleNamespace(dits=[pipeline.transformer], dit_names=["transformer"])
-
-        backend._load_weights_from_host_weight_cache(pipeline, modules, plan)
-
-        assert pipeline.transformer.post_load_calls == 0
-        assert pipeline.transformer.proj.weight is old_parameter
-        assert pipeline.transformer.proj.weight.runtime_marker == "preserved"
-        assert torch.equal(pipeline.transformer.proj.weight, torch.full((2, 2), 9.0))
-        backend._release_mmap_handles()
-
-    def test_host_weight_cache_mapping_failure_leaves_ordinary_tensors_intact(
-        self,
-        tmp_path,
-        patched_offload_runtime,
-    ):
-        pipeline = nn.Module()
-        pipeline.transformer = nn.Linear(2, 2, bias=False)
-        old_parameter = pipeline.transformer.weight
-        plan = HostWeightPlan(
-            backing_kind="host_weight_cache",
-            bindings={
-                "transformer.weight": TensorBinding(
-                    storage_key="transformer.weight",
-                    file_path=str(tmp_path / "missing.safetensors"),
-                )
-            },
-            runtime_layout_key="runtime-layout",
-            expected_file_digests={str(tmp_path / "missing.safetensors"): "unused"},
-        )
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                dlo_use_allgather=False,
-            ),
-            torch.device("cpu"),
-            host_weight_plan=plan,
-        )
-        modules = SimpleNamespace(dits=[pipeline.transformer], dit_names=["transformer"])
-
-        with pytest.raises(Exception, match="missing.safetensors"):
-            backend._load_weights_from_host_weight_cache(pipeline, modules, plan)
-
-        assert pipeline.transformer.weight is old_parameter
-
-    def test_host_weight_cache_digest_change_leaves_ordinary_tensors_intact(
-        self,
-        tmp_path,
-        patched_offload_runtime,
-    ):
-        pipeline = nn.Module()
-        pipeline.transformer = nn.Linear(2, 2, bias=False)
-        old_parameter = pipeline.transformer.weight
-        old_value = old_parameter.detach().clone()
-        weight_file = tmp_path / "runtime.safetensors"
-        save_file({"transformer.weight": torch.full((2, 2), 9.0)}, str(weight_file))
-        expected_digest = file_digest(weight_file)
-        with weight_file.open("r+b") as handle:
-            handle.seek(-1, os.SEEK_END)
-            original = handle.read(1)
-            handle.seek(-1, os.SEEK_END)
-            handle.write(bytes([original[0] ^ 0xFF]))
-        plan = HostWeightPlan(
-            backing_kind="host_weight_cache",
-            bindings={
-                "transformer.weight": TensorBinding(
-                    storage_key="transformer.weight",
-                    file_path=str(weight_file),
-                )
-            },
-            runtime_layout_key="runtime-layout",
-            expected_file_digests={str(weight_file): expected_digest},
-        )
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                dlo_use_allgather=False,
-            ),
-            torch.device("cpu"),
-            host_weight_plan=plan,
-        )
-        modules = SimpleNamespace(dits=[pipeline.transformer], dit_names=["transformer"])
-
-        with pytest.raises(RuntimeError, match="changed after loader validation"):
-            backend._load_weights_from_host_weight_cache(pipeline, modules, plan)
-
-        assert pipeline.transformer.weight is old_parameter
-        assert torch.equal(pipeline.transformer.weight, old_value)
-
     def test_runs_model_post_load_hook(self, tmp_path, patched_offload_runtime):
         pipeline = _MmapPostLoadPipeline()
         weights = {name: torch.ones(param.shape, dtype=torch.bfloat16) for name, param in pipeline.named_parameters()}
@@ -966,7 +788,7 @@ class TestMmapWeightLoading:
             backing_kind="checkpoint_mmap",
             bindings={
                 name: TensorBinding(
-                    storage_key=name,
+                    checkpoint_key=name,
                     file_path=str(weight_file),
                 )
                 for name in weights
@@ -1037,292 +859,6 @@ class TestMmapWeightLoading:
         assert all(hook.rank_local_mmap for group in backend._all_hook_groups for hook in group)
         backend.disable()
 
-    def test_host_weight_cache_enable_reclaims_private_weights_before_hook_setup(
-        self,
-        tmp_path,
-        patched_offload_runtime,
-    ):
-        class Transformer(nn.Module):
-            _layerwise_offload_blocks_attrs = ["blocks"]
-
-            def __init__(self):
-                super().__init__()
-                self.blocks = nn.ModuleList([nn.Linear(2, 2, bias=False) for _ in range(2)])
-
-        class Pipeline(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.transformer = Transformer()
-
-        pipeline = Pipeline()
-        weights = {name: torch.full_like(param, 4) for name, param in pipeline.named_parameters()}
-        weight_file = tmp_path / "runtime.safetensors"
-        save_file(weights, str(weight_file))
-        plan = HostWeightPlan(
-            backing_kind="host_weight_cache",
-            bindings={name: TensorBinding(storage_key=name, file_path=str(weight_file)) for name in weights},
-            runtime_layout_key="runtime-layout",
-            expected_file_digests={str(weight_file): file_digest(weight_file)},
-        )
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                dlo_use_allgather=False,
-            ),
-            torch.device("cpu"),
-            host_weight_plan=plan,
-        )
-        cleanup_calls: list[None] = []
-        backend._cleanup_after_loading = lambda: cleanup_calls.append(None)  # type: ignore[method-assign]
-
-        backend.enable(pipeline)
-
-        assert backend._using_rank_local_mmap
-        # One reclamation immediately after remap, plus the existing final
-        # post-hook cleanup.
-        assert len(cleanup_calls) == 2
-        backend.disable()
-
-    def test_host_weight_cache_enable_falls_back_to_ordinary_tensors(
-        self,
-        tmp_path,
-        patched_offload_runtime,
-    ):
-        class Transformer(nn.Module):
-            _layerwise_offload_blocks_attrs = ["blocks"]
-
-            def __init__(self):
-                super().__init__()
-                self.blocks = nn.ModuleList([nn.Linear(2, 2, bias=False) for _ in range(2)])
-
-        class Pipeline(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.transformer = Transformer()
-
-        pipeline = Pipeline()
-        weight_file = tmp_path / "runtime.safetensors"
-        weights = {name: torch.full_like(param, 4) for name, param in pipeline.named_parameters()}
-        save_file(weights, str(weight_file))
-        plan = HostWeightPlan(
-            backing_kind="host_weight_cache",
-            bindings={name: TensorBinding(storage_key=name, file_path=str(weight_file)) for name in weights},
-            runtime_layout_key="runtime-layout",
-            expected_file_digests={str(weight_file): "stale-digest"},
-        )
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                dlo_use_allgather=False,
-            ),
-            torch.device("cpu"),
-            host_weight_plan=plan,
-        )
-
-        backend.enable(pipeline)
-
-        assert backend.enabled
-        assert backend.host_weight_plan is None
-        assert not backend._using_mmap
-        assert not backend._using_rank_local_mmap
-        backend.disable()
-
-    def test_disable_synchronizes_allgather_device_work(
-        self,
-        monkeypatch: pytest.MonkeyPatch,
-        patched_offload_runtime,
-    ):
-        synchronize_calls: list[None] = []
-        monkeypatch.setattr(
-            dist_backend_module.current_omni_platform,
-            "synchronize",
-            lambda: synchronize_calls.append(None),
-        )
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                dlo_use_allgather=True,
-            ),
-            torch.device("cpu"),
-        )
-        backend.enabled = True
-        backend._using_rank_local_mmap = False
-
-        backend.disable()
-
-        assert synchronize_calls == [None]
-
-    def test_host_weight_cache_registration_uses_budget_and_releases_before_mmap(
-        self,
-        monkeypatch: pytest.MonkeyPatch,
-        patched_offload_runtime,
-    ):
-        events: list[str] = []
-
-        class Registration:
-            total_bytes = 4096
-            region_count = 1
-
-            @staticmethod
-            def close() -> list[str]:
-                events.append("unregister")
-                return []
-
-        create_calls: list[tuple[dict[str, list[torch.Tensor]], int]] = []
-
-        def register(sources, *, device, max_bytes):
-            assert device == torch.device("cuda")
-            create_calls.append((sources, max_bytes))
-            return Registration()
-
-        monkeypatch.setattr(dist_backend_module, "register_host_mappings", register)
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=True,
-                dlo_use_allgather=False,
-                dlo_host_weight_cache_pin_limit_gib=1.5,
-            ),
-            torch.device("cuda"),
-        )
-        sources = {"runtime.safetensors": [torch.ones(1)]}
-        backend._host_weight_cache_mapped_sources = sources
-
-        assert backend._try_register_host_weight_cache_mmap()
-        assert create_calls == [(sources, int(1.5 * 1024**3))]
-
-        backend.enabled = True
-        backend._using_rank_local_mmap = True
-        backend._mmap_file_cache = {}
-        backend._release_mmap_handles = lambda: events.append("mmap")  # type: ignore[method-assign]
-        backend.disable()
-
-        assert events == ["unregister", "mmap"]
-
-    def test_host_weight_cache_registration_defaults_to_pinned_memory_policy(
-        self,
-        monkeypatch: pytest.MonkeyPatch,
-        patched_offload_runtime,
-    ):
-        class Registration:
-            total_bytes = 4096
-            region_count = 1
-
-            @staticmethod
-            def close() -> list[str]:
-                return []
-
-        registration = Registration()
-        create_calls: list[tuple[dict[str, list[torch.Tensor]], int | None]] = []
-
-        def register(sources, *, device, max_bytes):
-            assert device == torch.device("cuda")
-            create_calls.append((sources, max_bytes))
-            return registration
-
-        monkeypatch.setattr(dist_backend_module, "register_host_mappings", register)
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=True,
-                dlo_use_allgather=False,
-                dlo_host_weight_cache_pin_limit_gib=0.0,
-            ),
-            torch.device("cuda"),
-        )
-        sources = {"runtime.safetensors": [torch.ones(1)]}
-        backend._host_weight_cache_mapped_sources = sources
-
-        assert backend._try_register_host_weight_cache_mmap()
-        assert create_calls == [(sources, None)]
-        assert backend._host_registration is registration
-
-    def test_host_weight_cache_registration_respects_disabled_pinned_memory(
-        self,
-        monkeypatch: pytest.MonkeyPatch,
-        patched_offload_runtime,
-    ):
-        monkeypatch.setattr(
-            dist_backend_module,
-            "register_host_mappings",
-            lambda *_args, **_kwargs: pytest.fail("registration must follow pin_cpu_memory=False"),
-        )
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=False,
-                dlo_use_allgather=False,
-                dlo_host_weight_cache_pin_limit_gib=0.0,
-            ),
-            torch.device("cuda"),
-        )
-        backend._host_weight_cache_mapped_sources = {"runtime.safetensors": [torch.ones(1)]}
-
-        assert not backend._try_register_host_weight_cache_mmap()
-        assert backend._host_registration is None
-
-    def test_host_weight_cache_transfer_is_configured_once_for_all_consumers(
-        self,
-        monkeypatch: pytest.MonkeyPatch,
-        patched_offload_runtime,
-    ):
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=True,
-                dlo_use_allgather=False,
-            ),
-            torch.device("cuda"),
-            host_weight_plan=HostWeightPlan(
-                backing_kind="host_weight_cache",
-                bindings={},
-            ),
-        )
-        backend._using_rank_local_mmap = True
-        backend._host_weight_cache_mapped_sources = {"runtime.safetensors": [torch.ones(1)]}
-        streamed_hook = SimpleNamespace(registered_mmap=False)
-        resident_group = SimpleNamespace(
-            registered_mmap=False,
-            _cpu_staging_buffers=[{"buffer": torch.ones(1)}],
-        )
-        backend._resident_layer_group = resident_group
-        register_calls: list[None] = []
-
-        def register() -> bool:
-            register_calls.append(None)
-            return True
-
-        monkeypatch.setattr(backend, "_try_register_host_weight_cache_mmap", register)
-
-        backend._configure_host_weight_cache_transfer([streamed_hook])  # type: ignore[list-item]
-
-        assert register_calls == [None]
-        assert streamed_hook.registered_mmap
-        assert resident_group.registered_mmap
-        assert resident_group._cpu_staging_buffers == []
-        assert backend._host_weight_cache_mapped_sources == {}
-
-    def test_host_weight_cache_registration_falls_back_on_unsupported_platform(
-        self,
-        patched_offload_runtime,
-    ):
-        backend = DistributedLayerwiseOffloadBackend(
-            OffloadConfig(
-                strategy=OffloadStrategy.DISTRIBUTED_LAYER_WISE,
-                pin_cpu_memory=True,
-                dlo_use_allgather=False,
-                dlo_host_weight_cache_pin_limit_gib=1.0,
-            ),
-            torch.device("cpu"),
-        )
-        backend._host_weight_cache_mapped_sources = {"runtime.safetensors": [torch.ones(1)]}
-
-        assert not backend._try_register_host_weight_cache_mmap()
-        assert backend._host_registration is None
-
 
 class TestGetBlocksFromDit:
     def test_get_blocks_from_dit_single_block_attr(self):
@@ -1628,7 +1164,7 @@ class TestMmapValidation:
         assert result.fallback_reason is None
         assert result.plan is not None
         assert result.plan.bindings["transformer.blocks.0.weight"] == TensorBinding(
-            storage_key="blocks.0.weight",
+            checkpoint_key="blocks.0.weight",
             file_path=str(checkpoint_file),
         )
         assert result.plan.planned_source_prefixes == frozenset({"transformer."})
@@ -1918,7 +1454,7 @@ class TestMmapValidation:
 
         assert result.plan is not None
         assert result.plan.bindings["transformer.proj_in.weight"] == TensorBinding(
-            storage_key="proj_in.weight",
+            checkpoint_key="proj_in.weight",
             file_path=str(checkpoint_file),
         )
 

tests/diffusion/test_diffusion_config_propagation.py

@@ -92,6 +92,30 @@ class TestParallelConfigPropagation:
         od = _roundtrip_diffusion_config(model="x", parallel_config=pc)
         assert od.num_gpus == 2
 
+    def test_dp_is_inferred_from_num_gpus(self):
+        pc = DiffusionParallelConfig(tensor_parallel_size=2, ulysses_degree=2)
+        od = OmniDiffusionConfig.from_kwargs(model="x", parallel_config=pc, num_gpus=8)
+        assert od.parallel_config.data_parallel_size == 2
+        assert od.parallel_config.world_size == 8
+
+    def test_explicit_dp_is_validated_against_num_gpus(self):
+        pc = DiffusionParallelConfig(tensor_parallel_size=2, data_parallel_size=2)
+        od = OmniDiffusionConfig.from_kwargs(model="x", parallel_config=pc, num_gpus=4)
+        assert od.parallel_config.data_parallel_size == 2
+
+        with pytest.raises(ValueError, match="does not match WORLD-derived value"):
+            OmniDiffusionConfig.from_kwargs(
+                model="x",
+                parallel_config=DiffusionParallelConfig(tensor_parallel_size=2, data_parallel_size=2),
+                num_gpus=8,
+            )
+
+    def test_hsdp_does_not_infer_ordinary_dp(self):
+        pc = DiffusionParallelConfig(use_hsdp=True, hsdp_shard_size=4)
+        od = OmniDiffusionConfig.from_kwargs(model="x", parallel_config=pc, num_gpus=4)
+        assert od.parallel_config.data_parallel_size == 1
+        assert od.parallel_config.world_size == 4
+
 
 class TestCreateDefaultDiffusion:
     """Verify engine_args structure from create_default_diffusion."""

tests/diffusion/test_worker_wrapper_base.py

@@ -12,13 +12,11 @@ This module tests the WorkerWrapperBase implementation:
 - Dynamic worker class extension
 """
 
-from types import SimpleNamespace
 from typing import Any
 
 import pytest
 from pytest_mock import MockerFixture
 
-import vllm_omni.diffusion.worker.diffusion_worker as worker_module
 from vllm_omni.diffusion.worker.diffusion_worker import (
     CustomPipelineWorkerExtension,
     DiffusionWorker,
@@ -206,29 +204,6 @@ class TestWorkerWrapperBaseDelegation:
         wrapper.worker.shutdown.assert_called_once()
         assert result is None
 
-    def test_worker_shutdown_disables_offloader_before_distributed_teardown(
-        self,
-        mocker: MockerFixture,
-        monkeypatch: pytest.MonkeyPatch,
-    ):
-        worker = object.__new__(DiffusionWorker)
-        offload_backend = mocker.Mock()
-        kv_transfer_manager = mocker.Mock()
-        worker.model_runner = SimpleNamespace(
-            offload_backend=offload_backend,
-            kv_transfer_manager=kv_transfer_manager,
-        )
-        events: list[str] = []
-        offload_backend.disable.side_effect = lambda: events.append("offloader")
-        kv_transfer_manager.shutdown_prefetch.side_effect = lambda: events.append("kv_transfer")
-        monkeypatch.setattr(worker_module, "destroy_distributed_env", lambda: events.append("distributed"))
-
-        worker.shutdown()
-
-        offload_backend.disable.assert_called_once_with()
-        kv_transfer_manager.shutdown_prefetch.assert_called_once_with()
-        assert events == ["offloader", "kv_transfer", "distributed"]
-
 
 # -------------------------------------------------------------------------
 # Tests: execute_method

tests/entrypoints/test_async_omni_diffusion_config.py

@@ -58,6 +58,23 @@ def test_default_stage_devices_from_sequence_parallel():
     assert stage_cfg["runtime"]["devices"] == "0,1,2,3"
 
 
+def test_default_stage_devices_and_dp_from_num_gpus():
+    """Resolve omitted DP before deriving devices from an explicit GPU count."""
+    stage_cfg = AsyncOmniEngine._create_default_diffusion_stage_cfg(
+        {
+            "num_gpus": 8,
+            "tensor_parallel_size": 2,
+            "ulysses_degree": 2,
+        }
+    )[0]
+
+    parallel_config = stage_cfg["engine_args"]["parallel_config"]
+    assert parallel_config.data_parallel_size == 2
+    assert parallel_config.world_size == 8
+    assert stage_cfg["engine_args"]["num_gpus"] == 8
+    assert stage_cfg["runtime"]["devices"] == "0,1,2,3,4,5,6,7"
+
+
 def test_default_stage_config_uses_parallel_size_kwargs():
     """Ensure default diffusion parallel_config uses CLI/API parallel sizes."""
     stage_cfg = AsyncOmniEngine._create_default_diffusion_stage_cfg(
@@ -76,8 +93,8 @@ def test_default_stage_config_uses_parallel_size_kwargs():
     assert parallel_config.enable_expert_parallel is True
 
 
-def test_default_stage_config_defaults_nullified_parallel_size_kwargs():
+def test_default_stage_config_preserves_omitted_dp_for_runtime_inference():
-    """Ensure nullified diffusion parallel-size kwargs fall back to defaults."""
+    """Keep omitted DP unresolved until the runtime WORLD size is known."""
     stage_cfg = AsyncOmniEngine._create_default_diffusion_stage_cfg(
         {
             "pipeline_parallel_size": None,
@@ -92,7 +109,7 @@ def test_default_stage_config_defaults_nullified_parallel_size_kwargs():
 
     parallel_config = stage_cfg["engine_args"]["parallel_config"]
     assert parallel_config.pipeline_parallel_size == 1
-    assert parallel_config.data_parallel_size == 1
+    assert parallel_config.data_parallel_size is None
     assert parallel_config.tensor_parallel_size == 1
     assert parallel_config.enable_expert_parallel is False
     assert stage_cfg["engine_args"]["enforce_eager"] is False
@@ -328,11 +345,10 @@ def test_serve_cli_forwards_distributed_offload_residency():
             "--omni",
             "--enable-distributed-layerwise-offload",
             "--dlo-no-use-allgather",
-            "--dlo-use-host-weight-cache",
-            "--dlo-host-weight-cache-pin-limit-gib",
-            "80",
             "--dlo-resident-layers",
             "20",
+            "--dlo-fp8-cache-dir",
+            "/tmp/dlo-fp8-cache",
         ]
     )
 
@@ -342,16 +358,12 @@ def test_serve_cli_forwards_distributed_offload_residency():
 
     assert args.enable_distributed_layerwise_offload is True
     assert args.dlo_use_allgather is False
-    assert args.dlo_use_host_weight_cache is True
-    assert args.dlo_host_weight_cache_pin_limit_gib == 80
     assert args.dlo_resident_layers == 20
+    assert args.dlo_fp8_cache_dir == "/tmp/dlo-fp8-cache"
     assert engine_args["enable_distributed_layerwise_offload"] is True
     assert engine_args["dlo_use_allgather"] is False
-    assert engine_args["dlo_use_host_weight_cache"] is True
-    assert engine_args["dlo_host_weight_cache_dir"] is None
-    assert engine_args["dlo_host_weight_cache_lock_timeout"] == 600.0
-    assert engine_args["dlo_host_weight_cache_pin_limit_gib"] == 80
     assert engine_args["dlo_resident_layers"] == 20
+    assert engine_args["dlo_fp8_cache_dir"] == "/tmp/dlo-fp8-cache"
 
 
 def test_serve_cli_accepts_diffusion_compile_controls():

vllm_omni/config/omni_config.py

@@ -11,7 +11,6 @@ later PRs cut consumers over to these classes.
 from __future__ import annotations
 
 import copy
-import math
 from collections.abc import Mapping
 from dataclasses import InitVar, dataclass, field, fields
 from functools import wraps
@@ -601,12 +600,17 @@ class OmniStageDiffusionParallelConfig(OmniStageParallelConfig):
             * self.cfg_parallel_size
         )
         if self.use_hsdp:
-            if self.tensor_parallel_size > 1 or self.data_parallel_size > 1:
+            incompatible = []
-                raise ValueError(
+            if self.tensor_parallel_size > 1:
-                    "HSDP (use_hsdp=True) cannot be used with TP or DP "
+                incompatible.append("TP")
-                    f"(tensor_parallel_size={self.tensor_parallel_size}, "
+            if self.data_parallel_size > 1:
-                    f"data_parallel_size={self.data_parallel_size})"
+                incompatible.append("DP")
-                )
+            if self.pipeline_parallel_size > 1:
+                incompatible.append("PP")
+            if self.enable_expert_parallel:
+                incompatible.append("EP")
+            if incompatible:
+                raise ValueError("HSDP (FSDP2) is not compatible with " + ", ".join(incompatible))
             if self.hsdp_shard_size == -1:
                 if other_parallel_world_size == 1:
                     raise ValueError("Cannot auto-calculate hsdp_shard_size when other parallelism is all 1")
@@ -688,11 +692,8 @@ class _DiffusionConfigProjection:
     enable_layerwise_offload: bool = False
     enable_distributed_layerwise_offload: bool = False
     dlo_use_allgather: bool = True
-    dlo_use_host_weight_cache: bool = False
-    dlo_host_weight_cache_dir: str | None = None
-    dlo_host_weight_cache_lock_timeout: float = Field(default=600.0, gt=0)
-    dlo_host_weight_cache_pin_limit_gib: float = Field(default=0.0, ge=0)
     dlo_resident_layers: int = Field(default=0, ge=0)
+    dlo_fp8_cache_dir: str | None = None
     pin_cpu_memory: bool = True
     diffusion_compile_granularity: Literal["regional", "full"] = "regional"
     diffusion_compile_dynamic: bool = Field(default=True, strict=True)
@@ -754,20 +755,6 @@ class _DiffusionConfigProjection:
         from vllm_omni.diffusion.diffusion_kv.config import parse_diffusion_kv_cache_mode
         from vllm_omni.quantization import build_quant_config
 
-        if not math.isfinite(self.dlo_host_weight_cache_lock_timeout):
-            raise ValueError("dlo_host_weight_cache_lock_timeout must be finite")
-        if not math.isfinite(self.dlo_host_weight_cache_pin_limit_gib):
-            raise ValueError("dlo_host_weight_cache_pin_limit_gib must be finite")
-
-        if self.dlo_host_weight_cache_pin_limit_gib and (
-            not self.enable_distributed_layerwise_offload or self.dlo_use_allgather
-        ):
-            raise ValueError("dlo_host_weight_cache_pin_limit_gib requires no-AllGather distributed layerwise offload")
-        if self.dlo_use_host_weight_cache and (not self.enable_distributed_layerwise_offload or self.dlo_use_allgather):
-            raise ValueError("dlo_use_host_weight_cache requires no-AllGather distributed layerwise offload")
-        if self.dlo_host_weight_cache_pin_limit_gib and not self.dlo_use_host_weight_cache:
-            raise ValueError("dlo_host_weight_cache_pin_limit_gib requires dlo_use_host_weight_cache=True")
-
         if self.tf_model_config is None:
             self.tf_model_config = TransformerConfig()
         elif isinstance(self.tf_model_config, Mapping):

vllm_omni/config/stage_config.py

@@ -382,11 +382,8 @@ class StageDeployConfig:
 
     enable_distributed_layerwise_offload: bool | None = None
     dlo_use_allgather: bool | None = None
-    dlo_use_host_weight_cache: bool | None = None
-    dlo_host_weight_cache_dir: str | None = None
-    dlo_host_weight_cache_lock_timeout: float | None = None
-    dlo_host_weight_cache_pin_limit_gib: float | None = None
     dlo_resident_layers: int | None = None
+    dlo_fp8_cache_dir: str | None = None
     # Diffusion-specific debug and observability knobs.
     enable_diffusion_pipeline_profiler: bool | None = None
 

vllm_omni/diffusion/data.py

@@ -154,8 +154,8 @@ class DiffusionParallelConfig:
     pipeline_parallel_size: int = 1
     """Number of pipeline parallel stages."""
 
-    data_parallel_size: int = 1
+    data_parallel_size: int | None = None
-    """Number of data parallel groups."""
+    """Number of data parallel groups. Inferred from WORLD when omitted."""
 
     tensor_parallel_size: int = 1
     """Number of tensor parallel groups."""
@@ -243,7 +243,7 @@ class DiffusionParallelConfig:
     def _validate_parallel_config(self) -> Self:
         """Validates the config relationships among the parallel strategies."""
         assert self.pipeline_parallel_size > 0, "Pipeline parallel size must be > 0"
-        assert self.data_parallel_size > 0, "Data parallel size must be > 0"
+        assert self.data_parallel_size is None or self.data_parallel_size > 0, "Data parallel size must be > 0"
         assert self.tensor_parallel_size > 0, "Tensor parallel size must be > 0"
         assert self.sequence_parallel_size > 0, "Sequence parallel size must be > 0"
         assert self.ulysses_degree > 0, "Ulysses degree must be > 0"
@@ -283,10 +283,12 @@ class DiffusionParallelConfig:
                 self.allgather_degree if self.allgather_degree > 1 else self.ulysses_degree * self.ring_degree
             )
 
-        # Calculate world_size from other parallelism dimensions
+        # Until the runtime WORLD size is known, an omitted DP dimension means
+        # one replica. OmniDiffusionConfig resolves it against num_gpus below.
+        data_parallel_size = self.data_parallel_size or 1
         other_parallel_world_size = (
             self.pipeline_parallel_size
-            * self.data_parallel_size
+            * data_parallel_size
             * self.tensor_parallel_size
             * self.sequence_parallel_size
             * self.cfg_parallel_size
@@ -297,13 +299,17 @@ class DiffusionParallelConfig:
         # 1. Standalone: when other parallelism is all 1, HSDP determines world_size
         # 2. Combined: HSDP overlays on top of other parallelism
         if self.use_hsdp:
-            if self.tensor_parallel_size > 1 or self.data_parallel_size > 1:
+            incompatible = []
-                raise ValueError(
+            if self.tensor_parallel_size > 1:
-                    "HSDP (use_hsdp=True) cannot be used with TP or DP "
+                incompatible.append("TP")
-                    f"(tensor_parallel_size={self.tensor_parallel_size}, "
+            if data_parallel_size > 1:
-                    f"data_parallel_size={self.data_parallel_size}). "
+                incompatible.append("DP")
-                    "Set tensor_parallel_size=1 and data_parallel_size=1 when using HSDP."
+            if self.pipeline_parallel_size > 1:
-                )
+                incompatible.append("PP")
+            if self.enable_expert_parallel:
+                incompatible.append("EP")
+            if incompatible:
+                raise ValueError("HSDP (FSDP2) is not compatible with " + ", ".join(incompatible))
             if self.hsdp_shard_size == -1:
                 # Auto-calculate: use other_parallel_world_size as shard_size
                 if self.hsdp_replicate_size <= 0:
@@ -338,6 +344,42 @@ class DiffusionParallelConfig:
         else:
             self.world_size = other_parallel_world_size
 
+    def resolve_data_parallel_size(self, world_size: int) -> int:
+        """Resolve or validate diffusion DP against the actual WORLD size."""
+        if world_size <= 0:
+            raise ValueError(f"WORLD size must be > 0, but got {world_size}")
+
+        if self.use_hsdp:
+            if self.data_parallel_size not in (None, 1):
+                raise ValueError("HSDP (FSDP2) requires data_parallel_size to be 1")
+            expected_world_size = self.hsdp_replicate_size * self.hsdp_shard_size
+            if world_size != expected_world_size:
+                raise ValueError(
+                    f"WORLD size ({world_size}) must equal HSDP size "
+                    f"({self.hsdp_replicate_size} x {self.hsdp_shard_size} = {expected_world_size})"
+                )
+            self.data_parallel_size = 1
+            self.world_size = world_size
+            return 1
+
+        non_dp_size = (
+            self.pipeline_parallel_size
+            * self.tensor_parallel_size
+            * self.sequence_parallel_size
+            * self.cfg_parallel_size
+        )
+        if world_size % non_dp_size != 0:
+            raise ValueError(f"WORLD size ({world_size}) must be divisible by non-DP parallel size ({non_dp_size})")
+        inferred_data_parallel_size = world_size // non_dp_size
+        if self.data_parallel_size is not None and self.data_parallel_size != inferred_data_parallel_size:
+            raise ValueError(
+                f"data_parallel_size ({self.data_parallel_size}) does not match WORLD-derived value "
+                f"({inferred_data_parallel_size})"
+            )
+        self.data_parallel_size = inferred_data_parallel_size
+        self.world_size = world_size
+        return inferred_data_parallel_size
+
     @classmethod
     def from_dict(cls, data: dict[str, Any]) -> "DiffusionParallelConfig":
         """
@@ -708,18 +750,12 @@ class OmniDiffusionConfig:
     # This avoids AllGather synchronization, while host memory follows the
     # loader's existing rank-local layout instead of adding a second DP shard.
     dlo_use_allgather: bool = True
-    # Opt into publishing/joining the transform-complete final-layout host
-    # weight cache instead of direct-checkpoint mmap.
-    dlo_use_host_weight_cache: bool = False
-    # Shared local-disk root. None selects ~/.cache/vllm-omni/dlo-host-weights.
-    dlo_host_weight_cache_dir: str | None = None
-    # Maximum wait for the per-layout POSIX writer lock.
-    dlo_host_weight_cache_lock_timeout: float = 600.0
-    # Optional maximum file-backed memory a worker may register with CUDA.
-    # Zero applies no additional ceiling; pin_cpu_memory controls registration.
-    dlo_host_weight_cache_pin_limit_gib: float = 0.0
     # Leading main-DiT blocks kept resident by distributed layerwise offload.
     dlo_resident_layers: int = 0
+    # Optional Phase-I normalized online-FP8 cache. A cache miss builds the
+    # artifact after the ordinary online loader; a valid cache hit uses the
+    # serialized FP8 checkpoint-mmap path.
+    dlo_fp8_cache_dir: str | None = None
 
     pin_cpu_memory: bool = True  # Use pinned memory for faster transfers when offloading
 
@@ -946,18 +982,6 @@ class OmniDiffusionConfig:
         )
 
     def __post_init__(self):
-        if not math.isfinite(self.dlo_host_weight_cache_lock_timeout) or self.dlo_host_weight_cache_lock_timeout <= 0:
-            raise ValueError("dlo_host_weight_cache_lock_timeout must be positive")
-        if not math.isfinite(self.dlo_host_weight_cache_pin_limit_gib) or self.dlo_host_weight_cache_pin_limit_gib < 0:
-            raise ValueError("dlo_host_weight_cache_pin_limit_gib must be non-negative")
-        if self.dlo_host_weight_cache_pin_limit_gib and (
-            not self.enable_distributed_layerwise_offload or self.dlo_use_allgather
-        ):
-            raise ValueError("dlo_host_weight_cache_pin_limit_gib requires no-AllGather distributed layerwise offload")
-        if self.dlo_use_host_weight_cache and (not self.enable_distributed_layerwise_offload or self.dlo_use_allgather):
-            raise ValueError("dlo_use_host_weight_cache requires no-AllGather distributed layerwise offload")
-        if self.dlo_host_weight_cache_pin_limit_gib and not self.dlo_use_host_weight_cache:
-            raise ValueError("dlo_host_weight_cache_pin_limit_gib requires dlo_use_host_weight_cache=True")
         if self.diffusion_compile_granularity not in {"regional", "full"}:
             raise ValueError(
                 "diffusion_compile_granularity must be 'regional' or 'full', "
@@ -1021,10 +1045,7 @@ class OmniDiffusionConfig:
             else:
                 self.num_gpus = 1
 
-        if self.num_gpus < self.parallel_config.world_size:
+        self.parallel_config.resolve_data_parallel_size(self.num_gpus)
-            raise ValueError(
-                f"num_gpus ({self.num_gpus}) < parallel_config.world_size ({self.parallel_config.world_size})"
-            )
 
         if self.diffusion_compile_granularity == "full":
             incompatible_features = []

vllm_omni/diffusion/distributed/__init__.py

@@ -6,11 +6,6 @@ from vllm_omni.diffusion.distributed.hsdp import (
     HSDPInferenceConfig,
     apply_hsdp_to_model,
 )
-from vllm_omni.diffusion.distributed.parallel_state import (
-    get_fs_group,
-    get_fully_shard_rank,
-    get_fully_shard_world_size,
-)
 from vllm_omni.diffusion.distributed.sp_plan import (
     SequenceParallelConfig,
     SequenceParallelInput,
@@ -46,8 +41,4 @@ __all__ = [
     "get_sharding_validator",
     "HSDPInferenceConfig",
     "apply_hsdp_to_model",
-    # FS (Fully Shard) utilities
-    "get_fs_group",
-    "get_fully_shard_rank",
-    "get_fully_shard_world_size",
 ]

vllm_omni/diffusion/distributed/autoencoders/distributed_vae_executor.py

@@ -9,8 +9,6 @@ import torch
 import torch.distributed as dist
 from vllm.logger import init_logger
 
-from vllm_omni.diffusion.distributed.parallel_state import get_dit_group
-
 logger = init_logger(__name__)
 
 
@@ -45,9 +43,10 @@ class DistributedVaeExecutor:
     """
 
     def __init__(self):
-        self.group = get_dit_group()
+        # VAE tiles are scheduled across the complete worker WORLD.
-        self.world_size = dist.get_world_size(self.group)
+        self.group = None
-        self.rank = dist.get_rank(self.group)
+        self.world_size = dist.get_world_size()
+        self.rank = dist.get_rank()
         self.parallel_size = 1
         self.parallel_mode = "tile"
 
@@ -189,7 +188,7 @@ class DistributedVaeMixin:
         if self.distributed_executor.parallel_size > pp_size:
             logger.warning(
                 f"vae_patch_parallel_size={self.distributed_executor.parallel_size} "
-                f"is greater than dit_group={world_size};"
+                f"is greater than WORLD={world_size};"
-                f" using dit_group size={world_size}"
+                f" using WORLD size={world_size}"
             )
         return True

vllm_omni/diffusion/distributed/group_coordinator.py

@@ -991,6 +991,18 @@ class PipelineGroupCoordinator(GroupCoordinator):
     def _pipeline_isend_skip(self, tensor: torch.tensor):
         return torch.distributed.isend(tensor, dst=self.skip_rank, group=self.skip_device_group)
 
+    def destroy(self):
+        groups = [*self.device_groups, *self.cpu_groups, self.skip_device_group]
+        seen = {id(self.device_group), id(self.cpu_group)}
+        for group in groups:
+            if group is not None and id(group) not in seen:
+                torch.distributed.destroy_process_group(group)
+                seen.add(id(group))
+        self.device_groups = []
+        self.cpu_groups = []
+        self.skip_device_group = None
+        super().destroy()
+
 
 class SequenceParallelGroupCoordinator(GroupCoordinator):
     def __init__(
@@ -1031,3 +1043,15 @@ class SequenceParallelGroupCoordinator(GroupCoordinator):
         self.ring_rank = torch.distributed.get_rank(self.ring_group)
         self.allgather_world_size = torch.distributed.get_world_size(self.allgather_group)
         self.allgather_rank = torch.distributed.get_rank(self.allgather_group)
+
+    def destroy(self):
+        groups = [self.ulysses_group, self.ring_group, self.allgather_group]
+        seen = {id(self.device_group), id(self.cpu_group)}
+        for group in groups:
+            if group is not None and id(group) not in seen:
+                torch.distributed.destroy_process_group(group)
+                seen.add(id(group))
+        self.ulysses_group = None
+        self.ring_group = None
+        self.allgather_group = None
+        super().destroy()

vllm_omni/diffusion/distributed/hsdp.py

@@ -15,12 +15,7 @@ from torch.distributed.fsdp import (
 )
 from vllm.logger import init_logger
 
-from vllm_omni.diffusion.distributed.parallel_state import (
+from vllm_omni.diffusion.distributed.parallel_state import get_world_group
-    get_fs_group,
-    get_fully_shard_rank,
-    get_fully_shard_world_size,
-    get_world_group,
-)
 from vllm_omni.platforms import current_omni_platform
 
 logger = init_logger(__name__)
@@ -54,36 +49,36 @@ class HSDPInferenceConfig:
 def _create_hsdp_mesh(
     device_type: str,
     replicate_size: int,
-    shard_pg: torch.distributed.ProcessGroup,
+    shard_size: int,
 ) -> DeviceMesh:
-    """Create a 2D DeviceMesh for HSDP using an existing ProcessGroup for the shard dimension.
+    """Create the FSDP2 DeviceMesh; it owns the HSDP process groups.
 
     Args:
         device_type: The device type (e.g., "cuda", "npu")
         replicate_size: Number of replica groups
-        shard_pg: The ProcessGroup for the shard dimension (from FS GroupCoordinator)
+        shard_size: Number of ranks in each FSDP shard group
 
     Returns:
         A 2D DeviceMesh with dimensions ("replicate", "shard")
     """
-    shard_size = torch.distributed.get_world_size(shard_pg)
     world_size = replicate_size * shard_size
+    actual_world_size = torch.distributed.get_world_size()
+    if world_size != actual_world_size:
+        raise ValueError(
+            f"HSDP mesh dimensions ({replicate_size} x {shard_size} = {world_size}) "
+            f"must equal WORLD size ({actual_world_size})"
+        )
 
     # Build 2D mesh tensor: shape (replicate_size, shard_size)
     # Ranks are arranged so that each row is a shard group
     mesh_tensor = torch.arange(world_size).reshape(replicate_size, shard_size)
 
-    # Create DeviceMesh with the shard ProcessGroup
-    # For the shard dimension, we reuse the existing FS ProcessGroup
     device_mesh = init_device_mesh(
         device_type,
         mesh_shape=(replicate_size, shard_size),
         mesh_dim_names=("replicate", "shard"),
     )
 
-    # Note: init_device_mesh creates new ProcessGroups internally.
-    # For consistency, we verify the mesh structure matches our FS group.
-    # In a future optimization, we could pass the existing ProcessGroups directly.
     logger.debug(
         "Created HSDP mesh: replicate_size=%d, shard_size=%d, mesh=%s",
         replicate_size,
@@ -119,34 +114,24 @@ def apply_hsdp_to_model(
     if not hsdp_config.enabled:
         raise ValueError("HSDP is not enabled in config")
 
-    # Use GroupCoordinator for distributed info
     world_group = get_world_group()
-    fs_group = get_fs_group()
-
     world_size = world_group.world_size
     rank = world_group.rank_in_group
-    fs_world_size = get_fully_shard_world_size()
-    fs_rank = get_fully_shard_rank()
 
     hsdp_replicate_size = hsdp_config.hsdp_replicate_size
     hsdp_shard_size = hsdp_config.hsdp_shard_size
 
-    # Validate that the FS group matches the HSDP shard size
+    if hsdp_replicate_size * hsdp_shard_size != world_size:
-    if fs_world_size != hsdp_shard_size:
         raise ValueError(
-            f"FS group world_size ({fs_world_size}) does not match "
+            f"HSDP mesh dimensions ({hsdp_replicate_size} x {hsdp_shard_size}) must equal WORLD size ({world_size})"
-            f"HSDP shard_size ({hsdp_shard_size}). "
-            "Ensure fully_shard_degree is set correctly in initialize_model_parallel."
         )
 
     logger.info(
-        "HSDP Inference: replicate_size=%d, shard_size=%d, world_size=%d, rank=%d, fs_world_size=%d, fs_rank=%d",
+        "HSDP Inference (FSDP2): replicate_size=%d, shard_size=%d, world_size=%d, rank=%d",
         hsdp_replicate_size,
         hsdp_shard_size,
         world_size,
         rank,
-        fs_world_size,
-        fs_rank,
     )
 
     # When the model contains FP8 parameters (online quantization), let FSDP
@@ -163,14 +148,14 @@ def apply_hsdp_to_model(
 
     device_type = current_omni_platform.device_type
 
-    # Create 2D DeviceMesh for HSDP using the FS group's ProcessGroup for shard dimension
+    # DeviceMesh is the single source of truth for FSDP2 shard/replicate groups
     # The mesh shape is (replicate, shard) where:
     # - replicate: groups of ranks that hold the same shard (for gradient all-reduce in training)
     # - shard: groups of ranks that each hold different shards (for parameter all-gather)
     device_mesh = _create_hsdp_mesh(
         device_type=device_type,
         replicate_size=hsdp_replicate_size,
-        shard_pg=fs_group.device_group,
+        shard_size=hsdp_shard_size,
     )
 
     hsdp_shard_conditions = getattr(model, "_hsdp_shard_conditions", None)

vllm_omni/diffusion/distributed/parallel_state.py

@@ -29,11 +29,11 @@ If you only need to use the distributed environment without model parallelism,
 """
 
 import inspect
+from math import prod
 
 import torch
 import torch.distributed
 import vllm.distributed.parallel_state as vllm_parallel_state
-from vllm.distributed.parallel_state import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 
 from vllm_omni.diffusion import envs
@@ -59,8 +59,6 @@ _SP: SequenceParallelGroupCoordinator | None = None
 _PP: PipelineGroupCoordinator | None = None
 _CFG: GroupCoordinator | None = None
 _DP: GroupCoordinator | None = None
-_FS: GroupCoordinator | None = None  # Fully Sharded (HSDP shard dimension)
-_DIT: GroupCoordinator | None = None
 
 # Rank-layout metadata for expert parallelism. This is not a process group;
 # it is reused by platform-specific runtimes that must build companion groups
@@ -184,7 +182,6 @@ class RankGenerator:
         pp: int,
         cfg: int,
         dp: int,
-        fs: int = 1,
         order: str = "tp-sp-pp-cfg-dp",
         rank_offset: int = 0,
     ) -> None:
@@ -193,9 +190,7 @@ class RankGenerator:
         self.pp = pp
         self.cfg = cfg
         self.dp = dp
-        self.fs = fs
         self.rank_offset = rank_offset
-        self.world_size = tp * sp * pp * cfg * dp
 
         self.name_to_size = {
             "tp": self.tp,
@@ -203,15 +198,10 @@ class RankGenerator:
             "pp": self.pp,
             "cfg": self.cfg,
             "dp": self.dp,
-            "fs": self.fs,
         }
         order = order.lower()
 
         for name in self.name_to_size.keys():
-            # Skip 'fs' validation - it's handled separately with independent_ranks=True
-            # and doesn't participate in the main orthogonal rank generation
-            if name == "fs":
-                continue
             if name not in order and self.name_to_size[name] != 1:
                 raise RuntimeError(
                     f"The size of ({name}) is ({self.name_to_size[name]}), "
@@ -234,7 +224,7 @@ class RankGenerator:
             mask[ordered_token.index(t)] = True
         return mask
 
-    def get_ranks(self, token, independent_ranks: bool = False):
+    def get_ranks(self, token):
         """Get rank group by input token.
 
         Arguments:
@@ -243,27 +233,11 @@ class RankGenerator:
                 to obtain multiple parallel types, we can use a hyphen
                 '-' to separate them. For example, if we want to obtain
                 the TP_DP group, the token should be 'tp-dp'.
-            independent_ranks (bool):
-                If True, generate independent rank groups that divide the world
-                into groups of the specified size. Used for FS (fully shard) groups
-                which operate independently from the main parallelism hierarchy.
         """
-        if independent_ranks and token == "fs":
-            # FS groups divide world into groups of size fs
-            # e.g., world_size=8, fs=4 -> [[0,1,2,3], [4,5,6,7]]
-            ranks = []
-            num_groups = self.world_size // self.fs
-            for i in range(num_groups):
-                group = list(range(i * self.fs + self.rank_offset, (i + 1) * self.fs + self.rank_offset))
-                ranks.append(group)
-            return ranks
-
         mask = self.get_mask(self.order, token)
-        ranks = generate_masked_orthogonal_rank_groups(self.world_size, self.ordered_size, mask)
+        ranks = generate_masked_orthogonal_rank_groups(prod(self.ordered_size), self.ordered_size, mask)
-        if self.rank_offset > 0:
+        if self.rank_offset:
-            for rank_group in ranks:
+            return [[rank + self.rank_offset for rank in rank_group] for rank_group in ranks]
-                for i in range(len(rank_group)):
-                    rank_group[i] += self.rank_offset
         return ranks
 
 
@@ -376,22 +350,6 @@ def get_data_parallel_rank():
     return get_dp_group().rank_in_group
 
 
-# FS (Fully Shard / HSDP shard dimension)
-def get_fs_group() -> GroupCoordinator:
-    assert _FS is not None, "fully shard group is not initialized"
-    return _FS
-
-
-def get_fully_shard_world_size():
-    """Return world size for the fully shard group."""
-    return get_fs_group().world_size
-
-
-def get_fully_shard_rank():
-    """Return my rank for the fully shard group."""
-    return get_fs_group().rank_in_group
-
-
 def is_dp_last_group():
     """Return True if in the last data parallel group, False otherwise."""
     return (
@@ -401,17 +359,6 @@ def is_dp_last_group():
     )
 
 
-def get_dit_world_size():
-    """Return world size for the DiT model."""
-    return (
-        get_data_parallel_world_size()
-        * get_classifier_free_guidance_world_size()
-        * get_sequence_parallel_world_size()
-        * get_pipeline_parallel_world_size()
-        * get_tensor_model_parallel_world_size()
-    )
-
-
 # * SET
 
 
@@ -498,7 +445,6 @@ def init_model_parallel_group(
         "expert",
         "sequence",
         "classifier_free_guidance",
-        "fully_shard",
     ], f"parallel_mode {parallel_mode} is not supported"
     if parallel_mode == "pipeline":
         return PipelineGroupCoordinator(
@@ -546,19 +492,6 @@ def init_vllm_model_parallel_group(
     )
 
 
-def init_dit_group(
-    dit_parallel_size: int,
-    backend: str,
-):
-    global _DIT
-    _DIT = torch.distributed.new_group(ranks=list(range(dit_parallel_size)), backend=backend)
-
-
-def get_dit_group():
-    assert _DIT is not None, "DIT group is not initialized"
-    return _DIT
-
-
 # adapted from https://github.com/feifeibear/long-context-attention/blob/main/yunchang/globals.py
 def set_seq_parallel_pg(
     sp_ulysses_degree: int,
@@ -737,8 +670,8 @@ def set_seq_parallel_pg(
     return ulysses_pg, ring_pg, allgather_pg
 
 
-def initialize_model_parallel(
+def _initialize_model_parallel(
-    data_parallel_size: int = 1,
+    data_parallel_size: int | None = None,
     cfg_parallel_size: int = 1,
     sequence_parallel_size: int | None = None,
     ulysses_degree: int = 1,
@@ -746,9 +679,8 @@ def initialize_model_parallel(
     allgather_degree: int = 1,
     tensor_parallel_size: int = 1,
     pipeline_parallel_size: int = 1,
-    fully_shard_degree: int = 1,
-    hsdp_replicate_size: int = 1,
     enable_expert_parallel: bool = False,
+    use_hsdp: bool = False,
     backend: str | None = None,
 ) -> None:
     if backend is None:
@@ -768,7 +700,6 @@ def initialize_model_parallel(
             (causal=False only). Mutually exclusive with ulysses/ring in v1.
         tensor_parallel_size: number of GPUs used for tensor parallelism.
         pipeline_parallel_size: number of GPUs used for pipeline parallelism.
-        fully_shard_degree: number of GPUs used for fully sharded data parallelism (HSDP shard dimension).
         backend: distributed backend of pytorch collective comm.
 
     Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
@@ -798,16 +729,20 @@ def initialize_model_parallel(
     ranks 8 to 15 belong to the second box.
     """
     # Get world size and rank. Ensure some consistencies.
-    assert torch.distributed.is_initialized()
+    if not torch.distributed.is_initialized():
+        raise RuntimeError("torch.distributed must be initialized before model parallel initialization")
+    if model_parallel_is_initialized():
+        raise RuntimeError("model parallel groups are already initialized")
     world_size: int = torch.distributed.get_world_size()
     backend = backend or torch.distributed.get_backend(get_world_group().device_group)
 
     if allgather_degree > 1:
-        assert ulysses_degree == 1 and ring_degree == 1, (
+        if ulysses_degree != 1 or ring_degree != 1:
-            "AllGather-KV (allgather_degree>1) is mutually exclusive with Ulysses/Ring in v1. "
+            raise ValueError(
-            f"Got ulysses_degree={ulysses_degree}, ring_degree={ring_degree}, "
+                "AllGather-KV (allgather_degree>1) is mutually exclusive with Ulysses/Ring in v1. "
-            f"allgather_degree={allgather_degree}."
+                f"Got ulysses_degree={ulysses_degree}, ring_degree={ring_degree}, "
-        )
+                f"allgather_degree={allgather_degree}."
+            )
 
     expected_sequence_parallel_size = allgather_degree if allgather_degree > 1 else ring_degree * ulysses_degree
     if sequence_parallel_size is None:
@@ -820,42 +755,38 @@ def initialize_model_parallel(
             f"but got {sequence_parallel_size}"
         )
 
-    dit_parallel_size = (
+    non_dp_size = cfg_parallel_size * sequence_parallel_size * pipeline_parallel_size * tensor_parallel_size
-        data_parallel_size * cfg_parallel_size * sequence_parallel_size * pipeline_parallel_size * tensor_parallel_size
+    if world_size % non_dp_size != 0:
-    )
+        raise ValueError(f"WORLD size ({world_size}) must be divisible by non-DP parallel size ({non_dp_size})")
-
+    if use_hsdp:
-    # Check for standalone HSDP: all non-HSDP parallelism dimensions are 1
+        if data_parallel_size not in (None, 1):
-    is_standalone_hsdp = dit_parallel_size == 1 and fully_shard_degree > 1
+            raise ValueError("HSDP (FSDP2) requires data_parallel_size to be 1")
-
+        if non_dp_size not in (1, world_size):
-    # For standalone HSDP: use (fully_shard_degree * hsdp_replicate_size) as dit_parallel_size
+            raise ValueError(f"HSDP non-DP parallel size must be 1 or WORLD size ({world_size}), but got {non_dp_size}")
-    # This ensures orthogonal rank generation works correctly for all HSDP workers
+        data_parallel_size = 1
-    if is_standalone_hsdp:
+    else:
-        dit_parallel_size = fully_shard_degree * hsdp_replicate_size
+        inferred_data_parallel_size = world_size // non_dp_size
-
+        if data_parallel_size is not None and data_parallel_size != inferred_data_parallel_size:
-    if world_size < dit_parallel_size:
+            raise ValueError(
-        raise RuntimeError(
+                f"data_parallel_size ({data_parallel_size}) does not match WORLD-derived value "
-            f"world_size ({world_size}) is less than "
+                f"({inferred_data_parallel_size})"
-            f"tensor_parallel_size ({tensor_parallel_size}) x "
+            )
-            f"pipeline_parallel_size ({pipeline_parallel_size}) x"
+        data_parallel_size = inferred_data_parallel_size
-            f"sequence_parallel_size ({sequence_parallel_size}) x"
-            f"cfg_parallel_size "
-            f"({cfg_parallel_size}) x"
-            f"data_parallel_size ({data_parallel_size})"
-        )
-
-    # For standalone HSDP, use (fully_shard_degree * hsdp_replicate_size) as data_parallel_size
-    # so that RankGenerator.world_size matches the actual number of workers
-    effective_dp_size = (fully_shard_degree * hsdp_replicate_size) if is_standalone_hsdp else data_parallel_size
 
     rank_generator: RankGenerator = RankGenerator(
         tensor_parallel_size,
         sequence_parallel_size,
         pipeline_parallel_size,
         cfg_parallel_size,
-        effective_dp_size,
+        data_parallel_size,
-        fs=fully_shard_degree,
         order="tp-sp-pp-cfg-dp",
     )
+
+    def get_rank_groups(token: str) -> list[list[int]]:
+        if use_hsdp and non_dp_size == 1:
+            return [[rank] for rank in range(world_size)]
+        return rank_generator.get_ranks(token)
+
     use_moe_parallel_mapping = False
     if enable_expert_parallel:
         od_config = get_forward_context().omni_diffusion_config
@@ -863,11 +794,11 @@ def initialize_model_parallel(
         if not use_moe_parallel_mapping:
             raise RuntimeError("Expert parallelism enabled for a non-MoE model")
 
-    sp_group_ranks = rank_generator.get_ranks("sp")
+    sp_group_ranks = get_rank_groups("sp")
     global _DP
     assert _DP is None, "data parallel group is already initialized"
     _DP = init_model_parallel_group(
-        group_ranks=rank_generator.get_ranks("dp"),
+        group_ranks=get_rank_groups("dp"),
         local_rank=get_world_group().local_rank,
         backend=backend,
         parallel_mode="data",
@@ -877,7 +808,7 @@ def initialize_model_parallel(
     global _CFG
     assert _CFG is None, "classifier_free_guidance group is already initialized"
     _CFG = init_model_parallel_group(
-        group_ranks=rank_generator.get_ranks("cfg"),
+        group_ranks=get_rank_groups("cfg"),
         local_rank=get_world_group().local_rank,
         backend=backend,
         parallel_mode="classifier_free_guidance",
@@ -885,7 +816,7 @@ def initialize_model_parallel(
     global _PP
     assert _PP is None, "pipeline model parallel group is already initialized"
     _PP = init_model_parallel_group(
-        group_ranks=rank_generator.get_ranks("pp"),
+        group_ranks=get_rank_groups("pp"),
         local_rank=get_world_group().local_rank,
         backend=backend,
         parallel_mode="pipeline",
@@ -899,7 +830,7 @@ def initialize_model_parallel(
         sp_ring_degree=ring_degree,
         sp_allgather_degree=allgather_degree,
         rank=get_world_group().rank_in_group,
-        world_size=dit_parallel_size,
+        world_size=world_size,
         sp_group_ranks=sp_group_ranks,
     )
     _SP = init_model_parallel_group(
@@ -925,7 +856,7 @@ def initialize_model_parallel(
         )
 
     assert vllm_parallel_state._TP is None, "Tensor parallel group is already initialized"
-    tp_group_ranks = rank_generator.get_ranks("tp")
+    tp_group_ranks = get_rank_groups("tp")
     if use_moe_parallel_mapping:
         vllm_parallel_state._TP = init_vllm_model_parallel_group(
             group_ranks=tp_group_ranks,
@@ -944,22 +875,14 @@ def initialize_model_parallel(
         # CFG is a diffusion-specific replica dimension. Fold it into vLLM DP
         # only when constructing the vLLM EP layout for expert-parallel paths.
         vllm_parallel_state._DP = init_vllm_model_parallel_group(
-            group_ranks=rank_generator.get_ranks("cfg-dp"),
+            group_ranks=get_rank_groups("cfg-dp"),
             local_rank=get_world_group().local_rank,
             backend=backend,
             group_name="dp",
         )
 
-    global _FS, _EXPERT_PARALLEL_GROUP_RANKS
+    global _EXPERT_PARALLEL_GROUP_RANKS
-    assert _FS is None, "fully shard group is already initialized"
+    _EXPERT_PARALLEL_GROUP_RANKS = get_rank_groups("tp-sp-cfg-dp")
-    _FS = init_model_parallel_group(
-        group_ranks=rank_generator.get_ranks("fs", independent_ranks=True),
-        local_rank=get_world_group().local_rank,
-        backend=backend,
-        parallel_mode="fully_shard",
-    )
-
-    _EXPERT_PARALLEL_GROUP_RANKS = rank_generator.get_ranks("tp-sp-cfg-dp")
     if use_moe_parallel_mapping:
         vllm_parallel_state._EP = init_vllm_model_parallel_group(
             group_ranks=_EXPERT_PARALLEL_GROUP_RANKS,
@@ -969,12 +892,63 @@ def initialize_model_parallel(
             use_all2all=True,
         )
 
-    init_dit_group(dit_parallel_size, backend)
+
+def initialize_model_parallel(
+    data_parallel_size: int | None = None,
+    cfg_parallel_size: int = 1,
+    sequence_parallel_size: int | None = None,
+    ulysses_degree: int = 1,
+    ring_degree: int = 1,
+    allgather_degree: int = 1,
+    tensor_parallel_size: int = 1,
+    pipeline_parallel_size: int = 1,
+    enable_expert_parallel: bool = False,
+    use_hsdp: bool = False,
+    backend: str | None = None,
+) -> None:
+    """Atomically initialize diffusion parallel groups.
+
+    Configuration is validated before group creation. Any failure tears down
+    groups created by this attempt, including vLLM-bound coordinators.
+    """
+    existing_groups = {
+        "dp": _DP,
+        "cfg": _CFG,
+        "sp": _SP,
+        "pp": _PP,
+        "vllm_tp": vllm_parallel_state._TP,
+        "vllm_dp": vllm_parallel_state._DP,
+        "vllm_pp": vllm_parallel_state._PP,
+        "vllm_ep": vllm_parallel_state._EP,
+        "vllm_pcp": vllm_parallel_state._PCP,
+    }
+    initialized_names = [name for name, group in existing_groups.items() if group is not None]
+    if initialized_names:
+        raise RuntimeError(
+            "model parallel state must be empty before initialization; found " + ", ".join(initialized_names)
+        )
+    try:
+        _initialize_model_parallel(
+            data_parallel_size=data_parallel_size,
+            cfg_parallel_size=cfg_parallel_size,
+            sequence_parallel_size=sequence_parallel_size,
+            ulysses_degree=ulysses_degree,
+            ring_degree=ring_degree,
+            allgather_degree=allgather_degree,
+            tensor_parallel_size=tensor_parallel_size,
+            pipeline_parallel_size=pipeline_parallel_size,
+            enable_expert_parallel=enable_expert_parallel,
+            use_hsdp=use_hsdp,
+            backend=backend,
+        )
+    except BaseException:
+        destroy_model_parallel()
+        raise
 
 
 def destroy_model_parallel():
     """Set the groups to none and destroy them."""
-    global _DP, _CFG, _SP, _PP, _FS, _EXPERT_PARALLEL_GROUP_RANKS
+    global _DP, _CFG, _SP, _PP, _EXPERT_PARALLEL_GROUP_RANKS
 
     if vllm_parallel_state._DP and vllm_parallel_state._DP is not _DP:
         vllm_parallel_state._DP.destroy()
@@ -1005,14 +979,14 @@ def destroy_model_parallel():
     vllm_parallel_state._EP = None
     _EXPERT_PARALLEL_GROUP_RANKS = None
 
+    if vllm_parallel_state._PP and vllm_parallel_state._PP is not _PP:
+        vllm_parallel_state._PP.destroy()
+    vllm_parallel_state._PP = None
+
     if _PP:
         _PP.destroy()
     _PP = None
 
-    if _FS:
-        _FS.destroy()
-    _FS = None
-
 
 def destroy_distributed_environment():
     global _WORLD

vllm_omni/diffusion/model_loader/checkpoint_adapters/direct_mmap.py

@@ -41,6 +41,8 @@ class _MiniMaxH3DirectMmapAdapter:
         target: torch.Tensor,
     ) -> DirectMmapTensorPolicy:
         del target
+        if getattr(self.pipeline, "_dlo_fp8_cache_normalized", False):
+            return DirectMmapTensorPolicy(allow_custom_loader=True)
         transform = None
         suffix = ".qkv_proj.weight"
         if runtime_name.endswith(suffix):

vllm_omni/diffusion/model_loader/diffusers_loader.py

@@ -8,13 +8,12 @@ import re
 import time
 from collections.abc import Generator, Iterable, Sequence
 from pathlib import Path
-from typing import cast
+from typing import Any, cast
 
 import torch
 from torch import nn
 from vllm.config.load import LoadConfig
 from vllm.logger import init_logger
-from vllm.model_executor.layers.linear import UnquantizedLinearMethod
 from vllm.model_executor.layers.quantization.base_config import QuantizeMethodBase
 from vllm.model_executor.model_loader.weight_utils import (
     download_safetensors_index_file_from_hf,
@@ -35,6 +34,12 @@ from vllm_omni.diffusion.distributed.hsdp import HSDPInferenceConfig, apply_hsdp
 from vllm_omni.diffusion.model_loader.checkpoint_adapters import (
     get_checkpoint_adapter,
 )
+from vllm_omni.diffusion.model_loader.fp8_cache import (
+    _transformer_quant_config,
+    build_normalized_fp8_cache,
+    is_online_fp8_config,
+    load_fp8_cache_manifest,
+)
 from vllm_omni.diffusion.model_loader.host_weight_plan import (
     HostWeightPlan,
     build_checkpoint_mmap_plan,
@@ -142,6 +147,10 @@ class DiffusersPipelineLoader:
         self.quant_config = od_config.quantization_config
         self.parallel_config = od_config.parallel_config
         self.host_weight_plan: HostWeightPlan | None = None
+        self._fp8_cache_dir: Path | None = None
+        self._fp8_cache_requested = False
+        self._fp8_cache_hit = False
+        self._fp8_cache_manifest: dict[str, Any] | None = None
 
     def take_host_weight_plan(self) -> HostWeightPlan | None:
         """Transfer the loader-produced plan to the offload backend."""
@@ -368,12 +377,61 @@ class DiffusersPipelineLoader:
         )
 
     def _get_weight_sources(self, model: nn.Module) -> tuple["ComponentSource", ...]:
-        return tuple(
+        sources = tuple(
             cast(
                 Iterable[DiffusersPipelineLoader.ComponentSource],
                 getattr(model, "weights_sources", ()),
             )
         )
+        if not self._fp8_cache_hit or self._fp8_cache_dir is None or self._fp8_cache_manifest is None:
+            return sources
+
+        cache_prefix = str(self._fp8_cache_manifest["source_prefix"])
+        cache_subfolder = str(self._fp8_cache_manifest.get("component_subfolder", "transformer"))
+        return tuple(
+            dataclasses.replace(
+                source,
+                model_or_path=str(self._fp8_cache_dir),
+                subfolder=cache_subfolder,
+                revision=None,
+            )
+            if source.prefix == cache_prefix
+            else source
+            for source in sources
+        )
+
+    def _prepare_fp8_cache(self) -> None:
+        """Select the Phase-I cache path before constructing the pipeline."""
+        cache_dir = getattr(self.od_config, "dlo_fp8_cache_dir", None)
+        if not cache_dir or not is_online_fp8_config(self.quant_config):
+            return
+        if not getattr(self.od_config, "enable_distributed_layerwise_offload", False):
+            logger.warning("Ignoring --dlo-fp8-cache-dir because distributed layerwise offload is disabled")
+            return
+        if not getattr(self.od_config, "dlo_use_allgather", True):
+            logger.warning("Ignoring --dlo-fp8-cache-dir because DLO AllGather is disabled")
+            return
+        if int(getattr(self.parallel_config, "tensor_parallel_size", 1)) != 1:
+            logger.warning("Ignoring --dlo-fp8-cache-dir because Phase-I cache requires TP=1")
+            return
+        if bool(getattr(self.parallel_config, "use_hsdp", False)):
+            logger.warning("Ignoring --dlo-fp8-cache-dir because Phase-I cache does not support HSDP")
+            return
+
+        self._fp8_cache_dir = Path(cache_dir)
+        self._fp8_cache_requested = True
+        self._fp8_cache_manifest = load_fp8_cache_manifest(self._fp8_cache_dir)
+        if self._fp8_cache_manifest is None:
+            logger.info("DLO normalized FP8 cache miss: %s", self._fp8_cache_dir)
+            return
+
+        self._fp8_cache_hit = True
+        self.quant_config = _transformer_quant_config()
+        self.od_config.quantization_config = self.quant_config
+        logger.info(
+            "DLO normalized FP8 cache hit: %s; selecting serialized FP8 checkpoint path",
+            self._fp8_cache_dir,
+        )
 
     def _get_expected_parameter_names(self, model: nn.Module) -> set[str]:
         """Return parameter names that should be covered by strict load checks."""
@@ -404,6 +462,7 @@ class DiffusersPipelineLoader:
         self.host_weight_plan = None
         if load_format is None:
             load_format = "default"
+        self._prepare_fp8_cache()
         # CPU offload + quantization: for offline-quantized models (e.g., AutoRound MXFP8),
         # weights are already quantized in the checkpoint — load directly on CPU.
         # For online quantization, load on device so quantization can run on accelerator,
@@ -414,7 +473,9 @@ class DiffusersPipelineLoader:
             is_offline = getattr(quant_cfg, "data_type", None) == "mx_fp" or getattr(
                 quant_cfg, "is_checkpoint_quantized", False
             )
-            if not is_offline:
+            if self._fp8_cache_hit:
+                logger.info("Normalized FP8 cache is already serialized; keeping model initialization on CPU")
+            elif not is_offline:
                 load_device = device.type
                 offload_after_quant = True
                 logger.info(
@@ -432,6 +493,11 @@ class DiffusersPipelineLoader:
                 )
             else:
                 model = self._init_from_load_format(load_format, target_device, custom_pipeline_name, is_hsdp=False)
+                if self._fp8_cache_hit:
+                    # The direct-mmap adapter must see this before plan
+                    # construction so normalized QKV tensors bypass the raw
+                    # checkpoint grouped-QKV transform.
+                    setattr(model, "_dlo_fp8_cache_normalized", True)
 
                 _dist_offload = getattr(self.od_config, "enable_distributed_layerwise_offload", False)
                 _use_ag = getattr(self.od_config, "dlo_use_allgather", True)
@@ -441,13 +507,10 @@ class DiffusersPipelineLoader:
                 _dp_size = int(getattr(self.parallel_config, "data_parallel_size", 1))
                 _sp_size = int(getattr(self.parallel_config, "sequence_parallel_size", 1))
                 _dlo_group_size = _dp_size if _dp_size > 1 else _sp_size
-                _select_host_weight_cache = (
-                    _dist_offload and not _use_ag and bool(getattr(self.od_config, "dlo_use_host_weight_cache", False))
-                )
 
                 plan_result = None
                 weight_sources = self._get_weight_sources(model)
-                if _dist_offload and not _select_host_weight_cache:
+                if _dist_offload:
                     modules = ModuleDiscovery.discover(model)
                     plan_result = build_checkpoint_mmap_plan(
                         model,
@@ -459,16 +522,6 @@ class DiffusersPipelineLoader:
                         online_quantization=_has_online_quant,
                     )
                     self.host_weight_plan = plan_result.plan
-                elif _select_host_weight_cache:
-                    # Direct checkpoint bindings can carry deferred transforms
-                    # (for example grouped-QKV reordering), so registering the
-                    # raw checkpoint mapping would not make every recurrent H2D
-                    # source directly copyable. Materialize once, apply every
-                    # post-load mutation, then publish/register the final layout.
-                    logger.info(
-                        "DLO host weight cache selected; using the ordinary loader "
-                        "once to publish a transform-complete final mmap layout"
-                    )
 
                 _skip_load = self.host_weight_plan is not None
 
@@ -510,8 +563,7 @@ class DiffusersPipelineLoader:
                         )
                     if _dist_offload and plan_result is not None:
                         logger.info(
-                            "DLO direct checkpoint mmap unavailable [%s]; using ordinary loader: %s",
+                            "DLO direct checkpoint mmap unavailable; using ordinary loader: %s",
-                            plan_result.fallback_code or "unknown",
                             plan_result.fallback_reason,
                         )
                     logger.debug("Loading weights on %s ...", load_device)
@@ -535,111 +587,22 @@ class DiffusersPipelineLoader:
                 model.to("cpu")
                 logger.info("Quantization complete, offloaded model back to CPU")
 
-        self._apply_skip_softmax_calibration(model)
+        if self._fp8_cache_requested and self._fp8_cache_dir is not None and not self._fp8_cache_hit:
-        model.eval()
-        self._maybe_build_host_weight_cache(model, load_format)
-        return model
-
-    def _maybe_build_host_weight_cache(self, model: nn.Module, load_format: str) -> None:
-        """Publish final ordinary-loader DiT tensors for no-AllGather DLO."""
-        if self.host_weight_plan is not None:
-            return
-        if not getattr(self.od_config, "enable_distributed_layerwise_offload", False):
-            return
-        if getattr(self.od_config, "dlo_use_allgather", True):
-            return
-        if not getattr(self.od_config, "dlo_use_host_weight_cache", False):
-            logger.info(
-                "DLO host weight cache not selected; retaining ordinary host tensors after checkpoint mmap fallback"
-            )
-            return
-
-        parallel_config = self.parallel_config
-        tp_size = int(getattr(parallel_config, "tensor_parallel_size", 1))
-        tp_rank = 0
-        if tp_size > 1:
             try:
-                from vllm.distributed import get_tensor_model_parallel_rank
+                build_normalized_fp8_cache(
-
+                    model,
-                tp_rank = int(get_tensor_model_parallel_rank())
+                    sources=self._get_weight_sources(model),
+                    cache_dir=self._fp8_cache_dir,
+                )
             except Exception as exc:
                 logger.warning(
-                    "DLO host weight cache unavailable [parallel_rank_unavailable]: "
+                    "DLO normalized FP8 cache build failed; retaining ordinary-loader path: %s",
-                    "cannot resolve TP rank for TP=%d: %s",
-                    tp_size,
                     exc,
+                    exc_info=True,
                 )
-                return
 
-        modules = ModuleDiscovery.discover(model)
+        self._apply_skip_softmax_calibration(model)
-        from vllm_omni.diffusion.model_loader.host_weight_cache import (
+        return model.eval()
-            DEFAULT_LOCK_TIMEOUT_SECONDS,
-            DEFAULT_SHARD_SIZE_BYTES,
-            build_host_weight_cache_plan,
-        )
-
-        loader_inputs = {
-            "model_class_name": getattr(self.od_config, "model_class_name", None),
-            "model_config": getattr(self.od_config, "model_config", None),
-            "diffusers_load_kwargs": getattr(self.od_config, "diffusers_load_kwargs", None),
-            "custom_pipeline_args": getattr(self.od_config, "custom_pipeline_args", None),
-            "lora_path": getattr(self.od_config, "lora_path", None),
-            "lora_scale": getattr(self.od_config, "lora_scale", None),
-            "lora_backend": getattr(self.od_config, "lora_backend", None),
-        }
-        sp_guard = {
-            "sequence_parallel_size": int(getattr(parallel_config, "sequence_parallel_size", 1)),
-            "ulysses_degree": int(getattr(parallel_config, "ulysses_degree", 1)),
-            "ring_degree": int(getattr(parallel_config, "ring_degree", 1)),
-            "allgather_degree": int(getattr(parallel_config, "allgather_degree", 1)),
-            "ulysses_mode": getattr(parallel_config, "ulysses_mode", "strict"),
-        }
-        model_identity = getattr(self.od_config, "model", None)
-        runtime_quantization = self.quant_config
-        if runtime_quantization is None and any(
-            (quant_method := getattr(module, "quant_method", None)) is not None
-            and not isinstance(quant_method, UnquantizedLinearMethod)
-            for module in model.modules()
-        ):
-            runtime_quantization = "module_quant_method"
-        result = build_host_weight_cache_plan(
-            model,
-            dit_modules=tuple(zip(modules.dit_names, modules.dits)),
-            loader_type=type(self),
-            cache_root=getattr(self.od_config, "dlo_host_weight_cache_dir", None),
-            lock_timeout_seconds=float(
-                getattr(self.od_config, "dlo_host_weight_cache_lock_timeout", DEFAULT_LOCK_TIMEOUT_SECONDS)
-            ),
-            max_shard_bytes=DEFAULT_SHARD_SIZE_BYTES,
-            model_identity=str(model_identity) if model_identity is not None else None,
-            revision=getattr(self.od_config, "revision", None),
-            runtime_dtype=getattr(self.od_config, "dtype", None),
-            load_format=load_format,
-            loader_inputs=loader_inputs,
-            tensor_parallel_size=tp_size,
-            tensor_parallel_rank=tp_rank,
-            sequence_parallel_guard=sp_guard,
-            use_hsdp=bool(getattr(parallel_config, "use_hsdp", False)),
-            enable_expert_parallel=bool(getattr(parallel_config, "enable_expert_parallel", False)),
-            quantization_config=runtime_quantization,
-            cfg_parallel_size=int(getattr(parallel_config, "cfg_parallel_size", 1)),
-            pipeline_parallel_size=int(getattr(parallel_config, "pipeline_parallel_size", 1)),
-        )
-        if result.plan is not None:
-            self.host_weight_plan = result.plan
-            logger.info(
-                "DLO host weight cache selected (layout=%s, TP=%d/%d, SP=%d)",
-                result.plan.runtime_layout_key[:12] if result.plan.runtime_layout_key else "unknown",
-                tp_rank,
-                tp_size,
-                sp_guard["sequence_parallel_size"],
-            )
-        else:
-            logger.warning(
-                "DLO host weight cache unavailable [%s]; retaining ordinary host tensors: %s",
-                result.fallback_code or "unknown",
-                result.fallback_reason or "unspecified reason",
-            )
 
     @staticmethod
     def _request_offload_after_quant(model: nn.Module) -> int:

vllm_omni/diffusion/model_loader/fp8_cache.py

@@ -0,0 +1,349 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Phase-I normalized FP8 cache support for DLO checkpoint mmap."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import shutil
+import uuid
+from pathlib import Path
+from typing import Any
+
+import torch
+from safetensors import safe_open
+from safetensors.torch import save_file
+from torch import nn
+from vllm.logger import init_logger
+
+from vllm_omni.diffusion.model_loader.host_weight_plan import (
+    _build_source_map,
+    _collect_required_dit_tensors,
+)
+from vllm_omni.quantization.component_config import ComponentQuantizationConfig
+
+logger = init_logger(__name__)
+
+CACHE_SCHEMA_VERSION = 1
+CACHE_KIND = "normalized_fp8_runtime"
+CACHE_MANIFEST_NAME = "manifest.json"
+CACHE_COMPLETE_NAME = "COMPLETE"
+CACHE_COMPONENT_SUBFOLDER = "transformer"
+
+
+def _transformer_quant_config() -> ComponentQuantizationConfig:
+    """Build the serialized FP8 config used by a normalized cache."""
+    from vllm.model_executor.layers.quantization.fp8 import Fp8Config
+
+    return ComponentQuantizationConfig(
+        {
+            "transformer": Fp8Config(
+                is_checkpoint_fp8_serialized=True,
+                activation_scheme="dynamic",
+            )
+        }
+    )
+
+
+def is_online_fp8_config(quant_config: Any) -> bool:
+    """Return whether the transformer component requests online FP8."""
+    if quant_config is None:
+        return False
+    if hasattr(quant_config, "resolve"):
+        quant_config = quant_config.resolve("transformer")
+    get_name = getattr(quant_config, "get_name", None)
+    if not callable(get_name) or str(get_name()).lower() != "fp8":
+        return False
+    return not bool(getattr(quant_config, "is_checkpoint_fp8_serialized", False))
+
+
+def _source_fingerprint(files: set[str]) -> str:
+    entries: list[dict[str, int | str]] = []
+    for filename in sorted(files):
+        stat = os.stat(filename)
+        entries.append(
+            {
+                "path": os.path.abspath(filename),
+                "size": stat.st_size,
+                "mtime_ns": stat.st_mtime_ns,
+            }
+        )
+    payload = json.dumps(entries, sort_keys=True, separators=(",", ":")).encode()
+    return hashlib.sha256(payload).hexdigest()
+
+
+def load_fp8_cache_manifest(cache_dir: str | os.PathLike[str] | None) -> dict[str, Any] | None:
+    """Return a validated Phase-I manifest, or ``None`` for a cache miss."""
+    if cache_dir is None:
+        return None
+    root = Path(cache_dir)
+    manifest_path = root / CACHE_MANIFEST_NAME
+    if not (root / CACHE_COMPLETE_NAME).is_file() or not manifest_path.is_file():
+        return None
+    try:
+        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+    except (OSError, ValueError):
+        return None
+    if not isinstance(manifest, dict):
+        return None
+    if manifest.get("schema_version") != CACHE_SCHEMA_VERSION:
+        return None
+    if manifest.get("cache_kind") != CACHE_KIND:
+        return None
+    if manifest.get("component") != CACHE_COMPONENT_SUBFOLDER:
+        return None
+    quantization = manifest.get("quantization")
+    if not isinstance(quantization, dict) or quantization != {
+        "method": "fp8",
+        "serialized": True,
+        "activation_scheme": "dynamic",
+    }:
+        return None
+    if manifest.get("component_subfolder") != CACHE_COMPONENT_SUBFOLDER:
+        return None
+    if manifest.get("source_prefix") != f"{CACHE_COMPONENT_SUBFOLDER}.":
+        return None
+
+    index_path = root / CACHE_COMPONENT_SUBFOLDER / "model.safetensors.index.json"
+    if not index_path.is_file():
+        return None
+    try:
+        index = json.loads(index_path.read_text(encoding="utf-8"))
+    except (OSError, ValueError):
+        return None
+    if not isinstance(index, dict):
+        return None
+    weight_map = index.get("weight_map")
+    if not isinstance(weight_map, dict) or not weight_map or not all(isinstance(key, str) for key in weight_map):
+        return None
+    if manifest.get("tensor_count") != len(weight_map):
+        return None
+    for filename in weight_map.values():
+        if not isinstance(filename, str):
+            return None
+        relative_path = Path(filename)
+        if (
+            relative_path.is_absolute()
+            or relative_path.name != filename
+            or relative_path.suffix != ".safetensors"
+            or not (root / CACHE_COMPONENT_SUBFOLDER / relative_path).is_file()
+        ):
+            return None
+
+    source_files = manifest.get("source_files")
+    source_fingerprint = manifest.get("source_fingerprint")
+    if not isinstance(source_files, list) or not source_files or not isinstance(source_fingerprint, str):
+        return None
+    try:
+        if _source_fingerprint({str(filename) for filename in source_files}) != source_fingerprint:
+            return None
+    except (OSError, TypeError):
+        return None
+    return manifest
+
+
+def _cache_key_for_runtime_name(runtime_name: str, prefix: str) -> str:
+    if not runtime_name.startswith(prefix):
+        raise ValueError(f"runtime tensor {runtime_name!r} is outside cache source prefix {prefix!r}")
+    return runtime_name[len(prefix) :]
+
+
+def build_normalized_fp8_cache(
+    model: nn.Module,
+    *,
+    sources: tuple[object, ...],
+    cache_dir: str | os.PathLike[str],
+) -> dict[str, Any]:
+    """Write a normalized FP8 cache from a completed online-FP8 model.
+
+    The builder writes tensors one at a time into individual safetensors shards,
+    so it does not create a second full-model state dict. The first cache build
+    still follows the existing online loader; warm launches consume the cache
+    through the direct DLO mmap path.
+    """
+    cache_root = Path(cache_dir)
+    existing_manifest = load_fp8_cache_manifest(cache_root)
+    if existing_manifest is not None:
+        return existing_manifest
+    if cache_root.exists() and any(cache_root.iterdir()):
+        raise FileExistsError(f"FP8 cache directory is not empty and is not valid: {cache_root}")
+    cache_root.parent.mkdir(parents=True, exist_ok=True)
+
+    dit_names = tuple(getattr(model, "_dit_modules", ()))
+    dit_names = tuple(name for name in dit_names if hasattr(model, name))
+    if len(dit_names) != 1:
+        raise ValueError(f"Phase-I FP8 cache requires exactly one DiT module, got {dit_names}")
+    dit_name = dit_names[0]
+    if dit_name != CACHE_COMPONENT_SUBFOLDER:
+        raise ValueError(
+            f"Phase-I FP8 cache requires a top-level {CACHE_COMPONENT_SUBFOLDER!r} module, got {dit_name!r}"
+        )
+    source_prefix = f"{dit_name}."
+    dit_module = model.get_submodule(dit_name)
+    planned_sources = tuple(source for source in sources if getattr(source, "prefix", "") == source_prefix)
+    if len(planned_sources) != 1:
+        raise ValueError(f"Phase-I FP8 cache requires one source for {source_prefix!r}, got {len(planned_sources)}")
+
+    source_map = _build_source_map(
+        sources=planned_sources,
+        model_path=None,
+        remap_fn=getattr(type(model), "_remap_ckpt_key", None),
+    )
+    required = _collect_required_dit_tensors(((dit_name, dit_module),))
+    quantized_modules: dict[str, nn.Module] = {}
+    try:
+        from vllm.model_executor.layers.quantization.online.fp8 import Fp8PerTensorOnlineLinearMethod
+    except ImportError as exc:  # pragma: no cover - vLLM 0.27 provides this class
+        raise RuntimeError("Phase-I FP8 cache requires vLLM's online FP8 implementation") from exc
+
+    for module_name, module in model.named_modules():
+        if not module_name.startswith(source_prefix):
+            continue
+        if isinstance(getattr(module, "quant_method", None), Fp8PerTensorOnlineLinearMethod):
+            if not hasattr(module, "weight_scale"):
+                raise ValueError(f"online FP8 module {module_name!r} has no generated weight_scale")
+            quantized_modules[module_name] = module
+
+    tmp_root = cache_root.parent / f".{cache_root.name}.tmp-{uuid.uuid4().hex}"
+    component_root = tmp_root / CACHE_COMPONENT_SUBFOLDER
+    component_root.mkdir(parents=True, exist_ok=False)
+    weight_map: dict[str, str] = {}
+    source_files: set[str] = set()
+    total_size = 0
+    part_index = 0
+
+    file_cache: dict[str, Any] = {}
+
+    def write_tensor(key: str, tensor: torch.Tensor) -> None:
+        nonlocal part_index, total_size
+        if key in weight_map:
+            raise ValueError(f"normalized FP8 cache has duplicate tensor key {key!r}")
+        tensor = tensor.detach().to("cpu").contiguous()
+        filename = f"part-{part_index:05d}.safetensors"
+        part_index += 1
+        save_file({key: tensor}, str(component_root / filename))
+        weight_map[key] = filename
+        total_size += tensor.numel() * tensor.element_size()
+
+    try:
+        for runtime_name, target in required.items():
+            binding_info = source_map.get(runtime_name)
+            module_name = runtime_name.removesuffix(".weight")
+            quant_module = quantized_modules.get(module_name) if runtime_name.endswith(".weight") else None
+            if runtime_name.endswith(".weight_scale"):
+                owner_name = runtime_name.removesuffix(".weight_scale")
+                if owner_name in quantized_modules:
+                    # Generated online scales are published in the dedicated
+                    # pass below; they have no raw checkpoint binding.
+                    continue
+            if quant_module is not None:
+                if binding_info is None:
+                    raise ValueError(f"no source binding for quantized weight {runtime_name!r}")
+                qweight = quant_module.weight.detach()
+                if qweight.ndim != 2:
+                    raise ValueError(f"Phase-I FP8 cache only supports 2D weights, got {runtime_name!r}")
+                # Online FP8 stores the final runtime weight as qweight.T. The
+                # serialized FP8 method expects the pre-process qweight shape.
+                qweight = qweight.t().contiguous()
+                # Normalized caches contain the post-checkpoint-adapter QKV
+                # order. The cache-hit adapter bypasses the raw-checkpoint
+                # grouped-QKV transform, then the serialized FP8 finalizer
+                # performs only the standard weight transpose.
+                write_tensor(binding_info[0], qweight)
+                source_files.add(binding_info[1])
+                continue
+
+            if binding_info is not None:
+                checkpoint_key, file_path = binding_info
+                source_files.add(file_path)
+                handle = file_cache.get(file_path)
+                if handle is None:
+                    handle = safe_open(file_path, framework="pt", device="cpu")
+                    file_cache[file_path] = handle
+                write_tensor(checkpoint_key, handle.get_tensor(checkpoint_key))
+            else:
+                # Constructor-derived persistent buffers have no checkpoint
+                # entry; preserve them as canonical cache tensors.
+                write_tensor(_cache_key_for_runtime_name(runtime_name, source_prefix), target)
+
+        for module_name, module in quantized_modules.items():
+            scale = module.weight_scale.detach()
+            logical_widths = getattr(module, "logical_widths", None)
+            if scale.numel() == 1 and logical_widths is not None and len(logical_widths) > 1:
+                # Serialized Fp8LinearMethod represents a fused per-tensor
+                # weight with one scale slot per logical shard. Online FP8
+                # produces one scalar for the fused tensor, so replicate that
+                # scalar to the offline runtime shape without changing its
+                # value.
+                scale = scale.reshape(1).expand(len(logical_widths)).contiguous()
+            write_tensor(
+                _cache_key_for_runtime_name(f"{module_name}.weight_scale", source_prefix),
+                scale,
+            )
+
+        index = {
+            "metadata": {"total_size": total_size},
+            "weight_map": dict(sorted(weight_map.items())),
+        }
+        (component_root / "model.safetensors.index.json").write_text(
+            json.dumps(index, indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        manifest = {
+            "schema_version": CACHE_SCHEMA_VERSION,
+            "cache_kind": CACHE_KIND,
+            "component": "transformer",
+            "component_subfolder": CACHE_COMPONENT_SUBFOLDER,
+            "source_prefix": source_prefix,
+            "quantization": {
+                "method": "fp8",
+                "serialized": True,
+                "activation_scheme": "dynamic",
+            },
+            "tensor_count": len(weight_map),
+            "source_fingerprint": _source_fingerprint(source_files),
+            "source_files": sorted(source_files),
+        }
+        (tmp_root / CACHE_MANIFEST_NAME).write_text(
+            json.dumps(manifest, indent=2, sort_keys=True) + "\n",
+            encoding="utf-8",
+        )
+        (tmp_root / CACHE_COMPLETE_NAME).write_text("phase1\n", encoding="utf-8")
+        for handle in file_cache.values():
+            close = getattr(handle, "__exit__", None)
+            if callable(close):
+                close(None, None, None)
+        try:
+            os.replace(tmp_root, cache_root)
+        except FileExistsError:
+            # Multiple DLO workers may build the same cache concurrently. If
+            # another worker published a complete artifact first, treat this
+            # publisher as a successful cache race rather than a build error.
+            published = load_fp8_cache_manifest(cache_root)
+            if published is None:
+                raise
+            shutil.rmtree(tmp_root, ignore_errors=True)
+            logger.info("Normalized FP8 cache was published concurrently at %s", cache_root)
+            return published
+        logger.info("Published normalized FP8 cache at %s (%d tensors)", cache_root, len(weight_map))
+        return manifest
+    except BaseException:
+        for handle in file_cache.values():
+            close = getattr(handle, "__exit__", None)
+            if callable(close):
+                close(None, None, None)
+        shutil.rmtree(tmp_root, ignore_errors=True)
+        raise
+
+
+__all__ = [
+    "CACHE_SCHEMA_VERSION",
+    "CACHE_KIND",
+    "CACHE_COMPONENT_SUBFOLDER",
+    "build_normalized_fp8_cache",
+    "is_online_fp8_config",
+    "load_fp8_cache_manifest",
+    "_transformer_quant_config",
+]

vllm_omni/diffusion/model_loader/host_weight_cache.py

@@ -1,574 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Build node-local host weight cache plans for diffusion DLO.
-
-``build_host_weight_cache_plan`` is the module's single entry point. It
-validates final ordinary-loader DiT tensors, joins or atomically publishes an
-immutable content-addressed entry, validates the mmap result, and returns a
-fail-closed host-weight plan. Reusable identity, hashing, locking, and file
-primitives live in :mod:`host_weight_cache_utils`.
-"""
-
-from __future__ import annotations
-
-import contextlib
-import hashlib
-import json
-import math
-import os
-import shutil
-import time
-import uuid
-from collections.abc import Mapping, Sequence
-from itertools import chain
-from pathlib import Path
-from typing import Any
-
-import torch
-from safetensors import safe_open
-from safetensors.torch import save_file
-from torch import nn
-from vllm.logger import init_logger
-
-from vllm_omni.diffusion.model_loader.host_weight_cache_utils import (
-    HostWeightCacheError,
-    IdentityNormalizationError,
-    RuntimeTensor,
-    canonical_json,
-    canonicalize_existing_local_path,
-    exclusive_lock,
-    file_digest,
-    fsync_directory,
-    fsync_file,
-    implementation_fingerprint,
-    normalize_identity,
-    remove_stale_temps,
-    runtime_content_digest,
-    split_shards,
-    tensor_metadata,
-)
-from vllm_omni.diffusion.model_loader.host_weight_plan import (
-    HostWeightPlan,
-    HostWeightPlanResult,
-    TensorBinding,
-)
-
-logger = init_logger(__name__)
-
-# Internal cache-format and I/O defaults, not environment variables. Public
-# configuration may override the root and lock timeout; chunk sizes stay private.
-# Bump the schema to isolate incompatible manifest layouts.
-HOST_WEIGHT_CACHE_SCHEMA_VERSION = 1
-# Keep the default shared across local stage replicas.
-DEFAULT_HOST_WEIGHT_CACHE_ROOT = "~/.cache/vllm-omni/dlo-host-weights"
-# Bound waits for another publisher instead of hanging initialization forever.
-DEFAULT_LOCK_TIMEOUT_SECONDS = 600.0
-# Avoid one model-sized output file and bound hashing/read working sets.
-DEFAULT_SHARD_SIZE_BYTES = 5 * 1024**3
-
-_SUPPORTED_DTYPES = {
-    torch.bool,
-    torch.uint8,
-    torch.int8,
-    torch.int16,
-    torch.int32,
-    torch.int64,
-    torch.float16,
-    torch.bfloat16,
-    torch.float32,
-    torch.float64,
-}
-for _dtype_name in ("float8_e4m3fn", "float8_e5m2"):
-    if (_dtype := getattr(torch, _dtype_name, None)) is not None:
-        _SUPPORTED_DTYPES.add(_dtype)
-
-
-def default_host_weight_cache_root() -> str:
-    """Return the default root, intentionally independent of VLLM_CACHE_ROOT."""
-    return os.path.expanduser(DEFAULT_HOST_WEIGHT_CACHE_ROOT)
-
-
-def _persistent_buffer(module: nn.Module, local_name: str) -> bool:
-    parent_path, _, leaf_name = local_name.rpartition(".")
-    owner = module.get_submodule(parent_path)
-    return leaf_name not in owner._non_persistent_buffers_set
-
-
-def _named_parameters_with_duplicates(module: nn.Module):
-    try:
-        return module.named_parameters(remove_duplicate=False)
-    except TypeError:  # pragma: no cover - compatibility with old torch
-        return module.named_parameters()
-
-
-def _named_buffers_with_duplicates(module: nn.Module):
-    try:
-        return module.named_buffers(remove_duplicate=False)
-    except TypeError:  # pragma: no cover - compatibility with old torch
-        return module.named_buffers()
-
-
-def _resolve_pipeline_tensor(pipeline: nn.Module, runtime_name: str) -> torch.Tensor | None:
-    parent_path, _, leaf_name = runtime_name.rpartition(".")
-    parent = pipeline.get_submodule(parent_path)
-    tensor = parent._parameters.get(leaf_name)
-    if tensor is None:
-        tensor = parent._buffers.get(leaf_name)
-    return tensor
-
-
-def _collect_runtime_tensors(
-    pipeline: nn.Module,
-    dit_modules: Sequence[tuple[str, nn.Module]],
-) -> list[RuntimeTensor]:
-    """Collect a complete, safely remappable view of final CPU DiT tensors.
-
-    Parameters and persistent buffers must resolve to the same objects through
-    the owning pipeline, occupy complete contiguous CPU storages, use supported
-    dtypes, and have no aliases inside or outside the discovered DiT modules.
-    The stable name ordering is reused by content hashing and publication.
-    """
-    records: dict[str, RuntimeTensor] = {}
-    for dit_name, dit_module in dit_modules:
-        for local_name, tensor in _named_parameters_with_duplicates(dit_module):
-            runtime_name = f"{dit_name}.{local_name}"
-            candidate = RuntimeTensor(runtime_name, tensor, "parameter")
-            existing = records.get(runtime_name)
-            if existing is not None and existing.tensor is not tensor:
-                raise HostWeightCacheError(
-                    "ambiguous_ownership",
-                    f"multiple DiT tensors resolve to {runtime_name!r}",
-                )
-            records[runtime_name] = candidate
-        for local_name, tensor in _named_buffers_with_duplicates(dit_module):
-            if not _persistent_buffer(dit_module, local_name):
-                continue
-            runtime_name = f"{dit_name}.{local_name}"
-            candidate = RuntimeTensor(runtime_name, tensor, "buffer")
-            existing = records.get(runtime_name)
-            if existing is not None and existing.tensor is not tensor:
-                raise HostWeightCacheError(
-                    "ambiguous_ownership",
-                    f"multiple DiT tensors resolve to {runtime_name!r}",
-                )
-            records[runtime_name] = candidate
-
-    if not records:
-        raise HostWeightCacheError(
-            "ambiguous_ownership",
-            "no DiT parameters or persistent buffers were discovered",
-        )
-
-    storage_owners: dict[tuple[int, int], str] = {}
-    for record in records.values():
-        tensor = record.tensor
-        try:
-            pipeline_tensor = _resolve_pipeline_tensor(pipeline, record.name)
-        except AttributeError as exc:
-            raise HostWeightCacheError(
-                "ambiguous_ownership",
-                f"DiT tensor {record.name!r} is not owned by the pipeline",
-            ) from exc
-        if pipeline_tensor is not tensor:
-            raise HostWeightCacheError(
-                "ambiguous_ownership",
-                f"DiT tensor {record.name!r} does not resolve to the discovered object",
-            )
-        if tensor.device.type != "cpu" or tensor.is_meta:
-            raise HostWeightCacheError(
-                "unsupported_tensor",
-                f"{record.name!r} must be a materialized CPU tensor, got {tensor.device}",
-            )
-        if tensor.layout != torch.strided:
-            raise HostWeightCacheError(
-                "unsupported_tensor",
-                f"{record.name!r} uses unsupported layout {tensor.layout}",
-            )
-        if not tensor.is_contiguous():
-            raise HostWeightCacheError(
-                "unsupported_tensor",
-                f"{record.name!r} is non-contiguous with stride {tensor.stride()}",
-            )
-        if tensor.dtype not in _SUPPORTED_DTYPES:
-            raise HostWeightCacheError(
-                "unsupported_tensor",
-                f"{record.name!r} uses unsupported dtype {tensor.dtype}",
-            )
-        if hasattr(tensor, "to_local"):
-            raise HostWeightCacheError(
-                "unsupported_tensor",
-                f"{record.name!r} is a distributed tensor",
-            )
-
-        tensor_nbytes = tensor.numel() * tensor.element_size()
-        storage = tensor.untyped_storage()
-        if tensor.storage_offset() != 0 or storage.nbytes() != tensor_nbytes:
-            raise HostWeightCacheError(
-                "unsupported_tensor",
-                f"{record.name!r} is a view into a larger storage",
-            )
-        if tensor_nbytes:
-            storage_id = (storage.data_ptr(), storage.nbytes())
-            owner = storage_owners.setdefault(storage_id, record.name)
-            if owner != record.name:
-                raise HostWeightCacheError(
-                    "unsupported_alias",
-                    f"{record.name!r} shares storage with {owner!r}",
-                )
-
-    # The ownership boundary is the complete pipeline, not only the discovered
-    # DiT traversal. Reject a DiT storage that is also registered by an encoder,
-    # VAE, resident component, or non-persistent buffer.
-    for pipeline_name, tensor in chain(
-        _named_parameters_with_duplicates(pipeline),
-        _named_buffers_with_duplicates(pipeline),
-    ):
-        if tensor.device.type != "cpu" or tensor.is_meta or tensor.numel() == 0:
-            continue
-        storage = tensor.untyped_storage()
-        owner = storage_owners.get((storage.data_ptr(), storage.nbytes()))
-        if owner is not None and owner != pipeline_name:
-            raise HostWeightCacheError(
-                "unsupported_alias",
-                f"cached tensor {owner!r} shares storage with pipeline tensor {pipeline_name!r}",
-            )
-
-    return [records[name] for name in sorted(records)]
-
-
-def _publish_entry(
-    *,
-    cache_root: Path,
-    entry_dir: Path,
-    cache_key: str,
-    layout_identity: dict[str, Any],
-    content_digest: str,
-    records: Sequence[RuntimeTensor],
-    max_shard_bytes: int,
-) -> None:
-    total_nbytes = sum(record.tensor.numel() * record.tensor.element_size() for record in records)
-    free_bytes = shutil.disk_usage(cache_root).free
-    required_bytes = total_nbytes + max(64 * 1024**2, total_nbytes // 100)
-    if free_bytes < required_bytes:
-        raise HostWeightCacheError(
-            "insufficient_disk",
-            f"host weight cache needs {required_bytes} bytes but only {free_bytes} are free under {cache_root}",
-        )
-
-    tmp_root = cache_root / ".tmp"
-    tmp_root.mkdir(parents=True, exist_ok=True)
-    temp_dir = tmp_root / f"{cache_key}.{os.getpid()}.{uuid.uuid4().hex}"
-    temp_dir.mkdir()
-    try:
-        shards = split_shards(records, max_shard_bytes)
-        tensor_manifest: dict[str, dict[str, Any]] = {}
-        file_manifest: dict[str, dict[str, Any]] = {}
-        shard_count = len(shards)
-        for index, shard in enumerate(shards, start=1):
-            filename = f"model-{index:05d}-of-{shard_count:05d}.safetensors"
-            path = temp_dir / filename
-            save_file(
-                {record.name: record.tensor.detach() for record in shard},
-                str(path),
-                metadata={"format": "pt", "dlo_host_weight_cache_schema": str(HOST_WEIGHT_CACHE_SCHEMA_VERSION)},
-            )
-            fsync_file(path)
-            file_manifest[filename] = {
-                "size": path.stat().st_size,
-                "sha256": file_digest(path),
-            }
-            for record in shard:
-                tensor_manifest[record.name] = {
-                    **tensor_metadata(record),
-                    "file": filename,
-                    "storage_key": record.name,
-                }
-
-        manifest = {
-            "schema_version": HOST_WEIGHT_CACHE_SCHEMA_VERSION,
-            "cache_key": cache_key,
-            "layout_identity": layout_identity,
-            "runtime_content_sha256": content_digest,
-            "total_tensor_bytes": total_nbytes,
-            "created_at_unix_seconds": time.time(),
-            "writer_pid": os.getpid(),
-            "files": file_manifest,
-            "tensors": tensor_manifest,
-        }
-        manifest_path = temp_dir / "manifest.json"
-        with manifest_path.open("w", encoding="utf-8") as handle:
-            json.dump(manifest, handle, sort_keys=True, separators=(",", ":"))
-            handle.flush()
-            os.fsync(handle.fileno())
-        fsync_directory(temp_dir)
-
-        invalid_dir: Path | None = None
-        if entry_dir.exists():
-            invalid_dir = tmp_root / f"{cache_key}.invalid.{os.getpid()}.{uuid.uuid4().hex}"
-            os.replace(entry_dir, invalid_dir)
-        os.replace(temp_dir, entry_dir)
-        fsync_directory(entry_dir.parent)
-        if invalid_dir is not None:
-            shutil.rmtree(invalid_dir, ignore_errors=True)
-    except Exception:
-        shutil.rmtree(temp_dir, ignore_errors=True)
-        raise
-
-
-def _validate_entry(
-    *,
-    entry_dir: Path,
-    cache_key: str,
-    layout_identity: dict[str, Any],
-    content_digest: str,
-    expected_records: Sequence[RuntimeTensor],
-) -> HostWeightPlan:
-    manifest_path = entry_dir / "manifest.json"
-    try:
-        with manifest_path.open(encoding="utf-8") as handle:
-            manifest = json.load(handle)
-    except (OSError, ValueError) as exc:
-        raise HostWeightCacheError("entry_rejected", f"cannot read {manifest_path}: {exc}") from exc
-
-    if manifest.get("schema_version") != HOST_WEIGHT_CACHE_SCHEMA_VERSION:
-        raise HostWeightCacheError("entry_rejected", "host weight cache schema mismatch")
-    if manifest.get("cache_key") != cache_key:
-        raise HostWeightCacheError("entry_rejected", "host weight cache key mismatch")
-    if manifest.get("layout_identity") != layout_identity:
-        raise HostWeightCacheError("entry_rejected", "host weight cache layout identity mismatch")
-    if manifest.get("runtime_content_sha256") != content_digest:
-        raise HostWeightCacheError("entry_rejected", "host weight cache content identity mismatch")
-
-    expected_metadata = {record.name: tensor_metadata(record) for record in expected_records}
-    tensor_manifest = manifest.get("tensors")
-    file_manifest = manifest.get("files")
-    if not isinstance(tensor_manifest, dict) or not isinstance(file_manifest, dict):
-        raise HostWeightCacheError("entry_rejected", "host weight cache manifest has invalid sections")
-    if set(tensor_manifest) != set(expected_metadata):
-        raise HostWeightCacheError("entry_rejected", "host weight cache tensor names differ from the loaded DiT")
-
-    for filename, metadata in file_manifest.items():
-        if Path(filename).name != filename or not isinstance(metadata, dict):
-            raise HostWeightCacheError("entry_rejected", f"invalid cache filename {filename!r}")
-        path = entry_dir / filename
-        try:
-            stat = path.stat()
-        except OSError as exc:
-            raise HostWeightCacheError("entry_rejected", f"missing cache shard {path}: {exc}") from exc
-        if stat.st_size != metadata.get("size") or file_digest(path) != metadata.get("sha256"):
-            raise HostWeightCacheError("entry_rejected", f"content digest mismatch for cache shard {path}")
-
-    bindings: dict[str, TensorBinding] = {}
-    mapped_records: list[RuntimeTensor] = []
-    with contextlib.ExitStack() as stack:
-        handles = {
-            filename: stack.enter_context(safe_open(entry_dir / filename, framework="pt", device="cpu"))
-            for filename in file_manifest
-        }
-        expected_keys_by_file: dict[str, set[str]] = {filename: set() for filename in file_manifest}
-        for record in expected_records:
-            stored = tensor_manifest.get(record.name)
-            if not isinstance(stored, dict):
-                raise HostWeightCacheError("entry_rejected", f"invalid tensor metadata for {record.name!r}")
-            filename = stored.get("file")
-            storage_key = stored.get("storage_key")
-            if filename not in handles or not isinstance(storage_key, str):
-                raise HostWeightCacheError("entry_rejected", f"invalid storage binding for {record.name!r}")
-            actual_metadata = {key: stored.get(key) for key in expected_metadata[record.name]}
-            if actual_metadata != expected_metadata[record.name]:
-                raise HostWeightCacheError("entry_rejected", f"metadata mismatch for {record.name!r}")
-            expected_keys_by_file[filename].add(storage_key)
-            try:
-                tensor = handles[filename].get_tensor(storage_key)
-            except Exception as exc:
-                raise HostWeightCacheError(
-                    "entry_rejected",
-                    f"cannot map {record.name!r} from {filename}: {exc}",
-                ) from exc
-            if tuple(tensor.shape) != tuple(record.tensor.shape) or tensor.dtype != record.tensor.dtype:
-                raise HostWeightCacheError("entry_rejected", f"mapped metadata mismatch for {record.name!r}")
-            mapped_records.append(RuntimeTensor(record.name, tensor, record.kind))
-            bindings[record.name] = TensorBinding(storage_key=storage_key, file_path=str(entry_dir / filename))
-
-        for filename, expected_keys in expected_keys_by_file.items():
-            if set(handles[filename].keys()) != expected_keys:
-                raise HostWeightCacheError("entry_rejected", f"unexpected tensor keys in cache shard {filename}")
-
-        if runtime_content_digest(mapped_records) != content_digest:
-            raise HostWeightCacheError("entry_rejected", "mapped runtime tensor content does not match the publisher")
-
-    return HostWeightPlan(
-        backing_kind="host_weight_cache",
-        bindings=bindings,
-        runtime_layout_key=cache_key,
-        expected_file_digests={
-            str(entry_dir / filename): metadata["sha256"] for filename, metadata in file_manifest.items()
-        },
-    )
-
-
-def build_host_weight_cache_plan(
-    pipeline: nn.Module,
-    *,
-    dit_modules: Sequence[tuple[str, nn.Module]],
-    loader_type: type,
-    cache_root: str | os.PathLike[str] | None,
-    lock_timeout_seconds: float,
-    max_shard_bytes: int,
-    model_identity: str | None,
-    revision: str | None,
-    runtime_dtype: Any,
-    load_format: str,
-    loader_inputs: Mapping[str, Any],
-    tensor_parallel_size: int,
-    tensor_parallel_rank: int,
-    sequence_parallel_guard: Mapping[str, Any],
-    use_hsdp: bool,
-    enable_expert_parallel: bool,
-    quantization_config: Any,
-    cfg_parallel_size: int,
-    pipeline_parallel_size: int,
-) -> HostWeightPlanResult:
-    """Build a host-weight plan from the host weight cache after ordinary loading.
-
-    This is the module's main entry point. It first rejects unsupported runtime
-    layouts, then discovers and validates the final CPU DiT tensors and derives
-    a content-addressed identity from their metadata, bytes, loader inputs, and
-    parallelism guards. A valid existing entry is joined immediately; otherwise
-    one process publishes the entry under a per-identity file lock and peers
-    validate it after the lock is released.
-
-    Success returns bindings to immutable safetensors mmap files. Any expected
-    incompatibility, lock, publication, or validation failure returns a
-    fail-closed result with no plan and a stable fallback code, leaving the
-    already-loaded pipeline tensors intact.
-    """
-    if load_format == "dummy":
-        return HostWeightPlanResult(
-            None,
-            "dummy/random weights do not have a reusable runtime identity",
-            "unsupported_load_format",
-        )
-    if use_hsdp:
-        return HostWeightPlanResult(
-            None, "HSDP/DTensor layouts are not host weight cache compatible", "unsupported_hsdp"
-        )
-    if enable_expert_parallel:
-        return HostWeightPlanResult(
-            None,
-            "expert-parallel weight ownership is outside host weight cache v1",
-            "unsupported_expert_parallel",
-        )
-    if quantization_config is not None:
-        return HostWeightPlanResult(
-            None,
-            "quantized runtime layouts are not supported by the first host weight cache version",
-            "unsupported_quantization",
-        )
-    if cfg_parallel_size != 1:
-        return HostWeightPlanResult(None, "CFG-parallel component ownership is not proven", "unsupported_cfg_parallel")
-    if pipeline_parallel_size != 1:
-        return HostWeightPlanResult(
-            None, "pipeline-parallel ownership is outside host weight cache v1", "unsupported_pp"
-        )
-    if not math.isfinite(lock_timeout_seconds) or lock_timeout_seconds <= 0:
-        return HostWeightPlanResult(None, "host weight cache lock timeout must be positive", "invalid_configuration")
-    if max_shard_bytes <= 0:
-        return HostWeightPlanResult(None, "host weight cache shard size must be positive", "invalid_configuration")
-
-    try:
-        records = _collect_runtime_tensors(pipeline, dit_modules)
-        content_digest = runtime_content_digest(records)
-        # The cache stores loader-produced ownership: TP coordinates differ,
-        # while equivalent DP replicas and SP ranks may share one entry. The
-        # conservative SP guard prevents reuse across unproven implementations.
-        layout_identity = {
-            "schema_version": HOST_WEIGHT_CACHE_SCHEMA_VERSION,
-            "model": (canonicalize_existing_local_path(model_identity) if model_identity is not None else None),
-            "revision": revision,
-            "runtime_dtype": str(runtime_dtype),
-            "load_format": load_format,
-            "loader_inputs": normalize_identity(loader_inputs),
-            "loader_implementation_sha256": implementation_fingerprint(loader_type, pipeline, dit_modules),
-            "components": [name for name, _ in dit_modules],
-            "tensor_parallel_size": tensor_parallel_size,
-            "tensor_parallel_rank": tensor_parallel_rank,
-            "sequence_parallel_guard": normalize_identity(sequence_parallel_guard),
-            "runtime_content_sha256": content_digest,
-        }
-        cache_key = hashlib.sha256(canonical_json(layout_identity)).hexdigest()
-        root = Path(cache_root or default_host_weight_cache_root()).expanduser().resolve()
-        entries_root = root / f"v{HOST_WEIGHT_CACHE_SCHEMA_VERSION}"
-        entry_dir = entries_root / cache_key
-        entries_root.mkdir(parents=True, exist_ok=True)
-
-        if entry_dir.is_dir():
-            try:
-                plan = _validate_entry(
-                    entry_dir=entry_dir,
-                    cache_key=cache_key,
-                    layout_identity=layout_identity,
-                    content_digest=content_digest,
-                    expected_records=records,
-                )
-                logger.info("Reusing DLO host weight cache entry %s from %s", cache_key[:12], entry_dir)
-                return HostWeightPlanResult(plan)
-            except HostWeightCacheError as exc:
-                logger.warning("DLO host weight cache entry %s will be rebuilt: %s", cache_key[:12], exc)
-
-        lock_path = root / ".locks" / f"{cache_key}.lock"
-        with exclusive_lock(lock_path, lock_timeout_seconds):
-            remove_stale_temps(root, cache_key)
-            if entry_dir.is_dir():
-                try:
-                    plan = _validate_entry(
-                        entry_dir=entry_dir,
-                        cache_key=cache_key,
-                        layout_identity=layout_identity,
-                        content_digest=content_digest,
-                        expected_records=records,
-                    )
-                    logger.info("Joined DLO host weight cache entry %s from %s", cache_key[:12], entry_dir)
-                    return HostWeightPlanResult(plan)
-                except HostWeightCacheError:
-                    pass
-
-            _publish_entry(
-                cache_root=root,
-                entry_dir=entry_dir,
-                cache_key=cache_key,
-                layout_identity=layout_identity,
-                content_digest=content_digest,
-                records=records,
-                max_shard_bytes=max_shard_bytes,
-            )
-            plan = _validate_entry(
-                entry_dir=entry_dir,
-                cache_key=cache_key,
-                layout_identity=layout_identity,
-                content_digest=content_digest,
-                expected_records=records,
-            )
-            logger.info(
-                "Published DLO host weight cache entry %s with %d tensors at %s",
-                cache_key[:12],
-                len(records),
-                entry_dir,
-            )
-            return HostWeightPlanResult(plan)
-    except HostWeightCacheError as exc:
-        return HostWeightPlanResult(None, str(exc), exc.code)
-    except IdentityNormalizationError as exc:
-        return HostWeightPlanResult(None, str(exc), "unstable_identity")
-    except Exception as exc:
-        return HostWeightPlanResult(None, f"host weight cache operation failed: {exc}", "cache_operation_failed")
-
-
-__all__ = [
-    "DEFAULT_LOCK_TIMEOUT_SECONDS",
-    "DEFAULT_HOST_WEIGHT_CACHE_ROOT",
-    "DEFAULT_SHARD_SIZE_BYTES",
-    "HOST_WEIGHT_CACHE_SCHEMA_VERSION",
-    "build_host_weight_cache_plan",
-    "default_host_weight_cache_root",
-]

vllm_omni/diffusion/model_loader/host_weight_cache_utils.py

@@ -1,287 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Reusable identity, hashing, and file helpers for the DLO host weight cache."""
-
-from __future__ import annotations
-
-import contextlib
-import dataclasses
-import errno
-import fcntl
-import hashlib
-import inspect
-import json
-import os
-import shutil
-import time
-from collections.abc import Iterator, Mapping, Sequence
-from dataclasses import dataclass
-from enum import Enum
-from pathlib import Path
-from typing import Protocol, TypeAlias, TypeVar
-
-import torch
-from torch import nn
-
-JsonScalar: TypeAlias = None | bool | int | float | str
-JsonValue: TypeAlias = JsonScalar | list["JsonValue"] | dict[str, "JsonValue"]
-PathIdentity: TypeAlias = str | os.PathLike[str] | os.PathLike[bytes]
-_HASH_CHUNK_BYTES = 64 * 1024**2
-_FILE_HASH_CHUNK_BYTES = 8 * 1024**2
-
-
-class IdentityNormalizationError(ValueError):
-    """Raised when a cache identity contains a process-unstable value."""
-
-
-@dataclass(frozen=True)
-class RuntimeTensor:
-    """One final runtime tensor and its manifest identity."""
-
-    name: str
-    tensor: torch.Tensor
-    kind: str
-
-
-class HostWeightCacheError(RuntimeError):
-    """Expected cache failure that should fall back with a stable code."""
-
-    def __init__(self, code: str, message: str):
-        super().__init__(message)
-        self.code = code
-
-
-class _HashUpdater(Protocol):
-    def update(self, data: bytes | bytearray | memoryview) -> object: ...
-
-
-_RuntimeTensorT = TypeVar("_RuntimeTensorT", bound=RuntimeTensor)
-
-
-def _type_identity(value: object) -> str:
-    value_type = value if isinstance(value, type) else type(value)
-    return f"{value_type.__module__}.{value_type.__qualname__}"
-
-
-def canonicalize_existing_local_path(value: PathIdentity) -> str:
-    """Resolve equivalent local paths while leaving model repository IDs alone."""
-    original = os.fsdecode(os.fspath(value))
-    candidate = Path(original).expanduser()
-    try:
-        if candidate.exists():
-            return str(candidate.resolve())
-    except OSError:
-        pass
-    return original
-
-
-def normalize_identity(value: object) -> JsonValue:
-    """Convert supported loader inputs into deterministic JSON-compatible data."""
-    if value is None or isinstance(value, (bool, int, float, str)):
-        return value
-    if isinstance(value, Enum):
-        return {"enum": _type_identity(value), "name": value.name}
-    if isinstance(value, torch.dtype):
-        return str(value)
-    if isinstance(value, os.PathLike):
-        return canonicalize_existing_local_path(value)
-    if isinstance(value, type):
-        return _type_identity(value)
-    if dataclasses.is_dataclass(value):
-        return normalize_identity(dataclasses.asdict(value))
-
-    model_dump = getattr(value, "model_dump", None)
-    if callable(model_dump):
-        try:
-            dumped = model_dump(mode="json")
-        except (TypeError, ValueError):
-            dumped = model_dump()
-        return normalize_identity(dumped)
-
-    if isinstance(value, Mapping):
-        normalized: dict[str, JsonValue] = {}
-        for key, item in value.items():
-            if not isinstance(key, str):
-                raise IdentityNormalizationError("host weight cache identity mappings require string keys")
-            normalized[key] = normalize_identity(item)
-        return {key: normalized[key] for key in sorted(normalized)}
-    if isinstance(value, (set, frozenset)):
-        return sorted((normalize_identity(item) for item in value), key=canonical_json)
-    if isinstance(value, (list, tuple)):
-        return [normalize_identity(item) for item in value]
-
-    raise IdentityNormalizationError(
-        f"host weight cache identity does not support values of type {_type_identity(value)}"
-    )
-
-
-def canonical_json(value: object) -> bytes:
-    """Serialize a supported identity with stable ordering and separators."""
-    return json.dumps(
-        normalize_identity(value),
-        sort_keys=True,
-        separators=(",", ":"),
-        ensure_ascii=True,
-    ).encode()
-
-
-def implementation_fingerprint(
-    loader_type: type,
-    pipeline: nn.Module,
-    dit_modules: Sequence[tuple[str, nn.Module]],
-) -> str:
-    """Hash relevant loader and module implementations for cache identity."""
-    objects: list[object] = [loader_type, type(pipeline)]
-    load_weights = getattr(type(pipeline), "load_weights", None)
-    if load_weights is not None:
-        objects.append(load_weights)
-    for _, dit_module in dit_modules:
-        objects.append(type(dit_module))
-        post_load = getattr(type(dit_module), "post_load_weights", None)
-        if post_load is not None:
-            objects.append(post_load)
-
-    digest = hashlib.sha256()
-    identities: set[str] = set()
-    for obj in objects:
-        identity = f"{getattr(obj, '__module__', '')}.{getattr(obj, '__qualname__', type(obj).__qualname__)}"
-        if identity in identities:
-            continue
-        identities.add(identity)
-        digest.update(identity.encode())
-        try:
-            digest.update(inspect.getsource(obj).encode())
-        except (OSError, TypeError):
-            pass
-    return digest.hexdigest()
-
-
-def tensor_metadata(record: RuntimeTensor) -> dict[str, JsonValue]:
-    """Return stable manifest metadata for one runtime tensor."""
-    tensor = record.tensor
-    return {
-        "kind": record.kind,
-        "shape": list(tensor.shape),
-        "dtype": str(tensor.dtype),
-        "nbytes": tensor.numel() * tensor.element_size(),
-        "layout": "contiguous",
-    }
-
-
-def _update_hash_with_tensor(digest: _HashUpdater, tensor: torch.Tensor) -> None:
-    byte_view = tensor.detach().reshape(-1).view(torch.uint8)
-    for offset in range(0, byte_view.numel(), _HASH_CHUNK_BYTES):
-        chunk = byte_view[offset : offset + _HASH_CHUNK_BYTES]
-        digest.update(memoryview(chunk.numpy()))
-
-
-def runtime_content_digest(records: Sequence[RuntimeTensor]) -> str:
-    """Hash ordered runtime tensor metadata and contents."""
-    digest = hashlib.sha256()
-    for record in records:
-        digest.update(canonical_json({"name": record.name, **tensor_metadata(record)}))
-        _update_hash_with_tensor(digest, record.tensor)
-    return digest.hexdigest()
-
-
-def file_digest(path: Path) -> str:
-    """Hash one cache file with a bounded read working set."""
-    digest = hashlib.sha256()
-    with path.open("rb") as handle:
-        while chunk := handle.read(_FILE_HASH_CHUNK_BYTES):
-            digest.update(chunk)
-    return digest.hexdigest()
-
-
-@contextlib.contextmanager
-def exclusive_lock(path: Path, timeout_seconds: float) -> Iterator[None]:
-    """Acquire one non-blocking file lock within a bounded wait."""
-    path.parent.mkdir(parents=True, exist_ok=True)
-    deadline = time.monotonic() + timeout_seconds
-    with path.open("a+b") as handle:
-        while True:
-            try:
-                fcntl.flock(handle.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
-                break
-            except OSError as exc:
-                if exc.errno not in (errno.EACCES, errno.EAGAIN):
-                    raise HostWeightCacheError("lock_failed", f"failed to lock {path}: {exc}") from exc
-                remaining = deadline - time.monotonic()
-                if remaining <= 0:
-                    raise HostWeightCacheError(
-                        "lock_timeout",
-                        f"timed out after {timeout_seconds:g}s waiting for host weight cache writer {path.name}",
-                    ) from exc
-                time.sleep(min(0.2, remaining))
-        try:
-            yield
-        finally:
-            fcntl.flock(handle.fileno(), fcntl.LOCK_UN)
-
-
-def fsync_file(path: Path) -> None:
-    """Persist one cache file before publication."""
-    with path.open("rb") as handle:
-        os.fsync(handle.fileno())
-
-
-def fsync_directory(path: Path) -> None:
-    """Persist directory entries needed for atomic publication."""
-    fd = os.open(path, os.O_RDONLY | getattr(os, "O_DIRECTORY", 0))
-    try:
-        os.fsync(fd)
-    finally:
-        os.close(fd)
-
-
-def split_shards(
-    records: Sequence[_RuntimeTensorT],
-    max_shard_bytes: int,
-) -> list[list[_RuntimeTensorT]]:
-    """Split ordered tensors without exceeding the target when possible."""
-    shards: list[list[_RuntimeTensorT]] = []
-    current: list[_RuntimeTensorT] = []
-    current_bytes = 0
-    for record in records:
-        nbytes = record.tensor.numel() * record.tensor.element_size()
-        if current and current_bytes + nbytes > max_shard_bytes:
-            shards.append(current)
-            current = []
-            current_bytes = 0
-        current.append(record)
-        current_bytes += nbytes
-    if current:
-        shards.append(current)
-    return shards
-
-
-def remove_stale_temps(cache_root: Path, cache_key: str) -> None:
-    """Remove abandoned temporary entries for one cache identity."""
-    temp_root = cache_root / ".tmp"
-    if not temp_root.is_dir():
-        return
-    for path in temp_root.glob(f"{cache_key}.*"):
-        if path.is_dir():
-            shutil.rmtree(path, ignore_errors=True)
-        else:
-            path.unlink(missing_ok=True)
-
-
-__all__ = [
-    "IdentityNormalizationError",
-    "JsonValue",
-    "HostWeightCacheError",
-    "RuntimeTensor",
-    "canonical_json",
-    "canonicalize_existing_local_path",
-    "exclusive_lock",
-    "file_digest",
-    "fsync_directory",
-    "fsync_file",
-    "implementation_fingerprint",
-    "normalize_identity",
-    "remove_stale_temps",
-    "runtime_content_digest",
-    "split_shards",
-    "tensor_metadata",
-]

vllm_omni/diffusion/model_loader/host_weight_plan.py

@@ -9,7 +9,6 @@ import json
 import os
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass
-from typing import Literal
 
 import torch
 from safetensors import safe_open
@@ -29,7 +28,7 @@ TensorTransform = Callable[[torch.Tensor], torch.Tensor]
 class TensorBinding:
     """One runtime tensor backed by one safetensors entry."""
 
-    storage_key: str
+    checkpoint_key: str
     file_path: str
     transform: TensorTransform | None = None
 
@@ -38,16 +37,9 @@ class TensorBinding:
 class HostWeightPlan:
     """Complete, prevalidated host backing consumed by an offload backend."""
 
-    backing_kind: Literal["checkpoint_mmap", "host_weight_cache"]
+    backing_kind: str
     bindings: dict[str, TensorBinding]
     planned_source_prefixes: frozenset[str] = frozenset()
-    runtime_layout_key: str | None = None
-    expected_file_digests: dict[str, str] | None = None
-
-    @property
-    def requires_backend(self) -> bool:
-        """Whether ordinary loading was skipped and this plan must be consumed."""
-        return self.backing_kind == "checkpoint_mmap"
 
 
 @dataclass(frozen=True)
@@ -56,7 +48,6 @@ class HostWeightPlanResult:
 
     plan: HostWeightPlan | None
     fallback_reason: str | None = None
-    fallback_code: str | None = None
 
 
 class _PlanIncompatibleError(RuntimeError):
@@ -267,11 +258,11 @@ def _validate_source_metadata(
         with safe_open(file_path, framework="pt", device="cpu") as handle:
             available = set(handle.keys())
             for runtime_name, target, binding in entries:
-                if binding.storage_key not in available:
+                if binding.checkpoint_key not in available:
                     raise _PlanIncompatibleError(
-                        f"checkpoint key {binding.storage_key!r} for {runtime_name!r} is missing from {file_path}"
+                        f"checkpoint key {binding.checkpoint_key!r} for {runtime_name!r} is missing from {file_path}"
                     )
-                source = handle.get_slice(binding.storage_key)
+                source = handle.get_slice(binding.checkpoint_key)
                 source_shape = tuple(source.get_shape())
                 source_dtype = _SAFETENSORS_DTYPES.get(source.get_dtype())
                 if source_shape != tuple(target.shape):
@@ -296,19 +287,11 @@ def build_checkpoint_mmap_plan(
 ) -> HostWeightPlanResult:
     """Build a complete direct-checkpoint plan or return a fallback reason."""
     if tensor_parallel_size != 1:
-        return HostWeightPlanResult(
+        return HostWeightPlanResult(None, f"TP={tensor_parallel_size} requires the ordinary loader")
-            None,
-            f"TP={tensor_parallel_size} requires the ordinary loader",
-            "unsupported_tp",
-        )
     if use_hsdp:
-        return HostWeightPlanResult(None, "HSDP requires the ordinary loader", "unsupported_hsdp")
+        return HostWeightPlanResult(None, "HSDP requires the ordinary loader")
     if online_quantization:
-        return HostWeightPlanResult(
+        return HostWeightPlanResult(None, "online quantization requires the ordinary loader")
-            None,
-            "online quantization requires the ordinary loader",
-            "unsupported_online_quantization",
-        )
 
     remap_fn = getattr(type(pipeline), "_remap_ckpt_key", None)
     if not callable(remap_fn):
@@ -345,14 +328,14 @@ def build_checkpoint_mmap_plan(
                 )
             checkpoint_key, file_path = model_to_ckpt[runtime_name]
             bindings[runtime_name] = TensorBinding(
-                storage_key=checkpoint_key,
+                checkpoint_key=checkpoint_key,
                 file_path=file_path,
                 transform=policy.transform if policy is not None else None,
             )
 
         _validate_source_metadata(required, bindings)
     except (OSError, ValueError, _PlanIncompatibleError) as exc:
-        return HostWeightPlanResult(None, str(exc), "checkpoint_plan_incompatible")
+        return HostWeightPlanResult(None, str(exc))
 
     return HostWeightPlanResult(
         HostWeightPlan(

vllm_omni/diffusion/models/minimax_h3/pipeline_minimax_h3.py

@@ -29,7 +29,7 @@ from vllm_omni.diffusion.cache.cachedit import (
 )
 from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
 from vllm_omni.diffusion.distributed.parallel_state import (
-    get_dit_group,
+    get_world_group,
     init_world_group,
 )
 from vllm_omni.diffusion.distributed.utils import get_local_device
@@ -423,7 +423,7 @@ def _validate_reference_image(image: Image.Image) -> None:
 def _dit_rank_world() -> tuple[Any, int, int]:
     if not dist.is_initialized():
         return None, 0, 1
-    group = get_dit_group()
+    group = get_world_group().device_group
     return group, dist.get_rank(group), dist.get_world_size(group)
 
 
@@ -1715,7 +1715,7 @@ class MiniMaxH3Pipeline(
                     dist.broadcast(
                         has_audio_tensor,
                         src=0,
-                        group=get_dit_group(),
+                        group=get_world_group().device_group,
                     )
                 has_audio = [bool(value) for value in has_audio_tensor.tolist()]
 

vllm_omni/diffusion/models/minimax_h3/vae.py

@@ -18,7 +18,7 @@ from transformers.dynamic_module_utils import get_class_from_dynamic_module
 from vllm_omni.diffusion.distributed.autoencoders.distributed_vae_executor import (
     DistributedVaeMixin,
 )
-from vllm_omni.diffusion.distributed.parallel_state import get_dit_group
+from vllm_omni.diffusion.distributed.parallel_state import get_world_group
 from vllm_omni.diffusion.offloader.module_residency import PinnedModuleStager
 
 from .packed_tokens import minimax_h3_patchify_video_latent
@@ -157,7 +157,7 @@ class MiniMaxH3VideoVAE(nn.Module, DistributedVaeMixin):
     ) -> None:
         if mode != "tile":
             raise ValueError(f"MiniMax H3 VAE supports its native tile parallel mode only, got {mode!r}")
-        group = get_dit_group()
+        group = get_world_group().device_group
         world_size = dist.get_world_size(group)
         rank = dist.get_rank(group)
         parallel_size = int(parallel_size)

vllm_omni/diffusion/offloader/__init__.py

@@ -71,9 +71,8 @@ def get_offload_backend(
     Args:
         od_config: OmniDiffusionConfig with offload settings
         device: Target device (auto-detected if None)
-        host_weight_plan: Exact loader-produced backing plan. Checkpoint plans
+        host_weight_plan: Exact loader-produced backing plan, if ordinary
-            may have skipped ordinary materialization; host weight cache plans
+            weight materialization was skipped.
-            describe already-valid ordinary tensors that can be remapped.
 
     Returns:
         OffloadBackend instance or None if offloading disabled
@@ -86,14 +85,10 @@ def get_offload_backend(
     # Extract and validate configuration
     config = OffloadConfig.from_od_config(od_config)
 
-    plan_requires_backend = host_weight_plan is not None and host_weight_plan.requires_backend
     if host_weight_plan is not None and config.strategy != OffloadStrategy.DISTRIBUTED_LAYER_WISE:
-        if plan_requires_backend:
+        raise RuntimeError(
-            raise RuntimeError(
+            "A loader-owned DLO host-weight plan was produced, but distributed layerwise offload is not selected"
-                "A loader-owned DLO host-weight plan was produced, but distributed layerwise offload is not selected"
+        )
-            )
-        logger.warning("Ignoring optional host weight cache plan because distributed layerwise offload is not selected")
-        host_weight_plan = None
 
     # Return None if no offloading requested
     if config.strategy == OffloadStrategy.NONE:
@@ -101,7 +96,7 @@ def get_offload_backend(
 
     # Validate platform (CUDA required for now)
     if not current_omni_platform.supports_cpu_offload() or current_omni_platform.get_device_count() < 1:
-        if plan_requires_backend:
+        if host_weight_plan is not None:
             raise RuntimeError(
                 "The loader skipped ordinary weight materialization for DLO, "
                 "but this platform cannot create the required offload backend"
@@ -117,7 +112,7 @@ def get_offload_backend(
         try:
             device = current_omni_platform.get_torch_device()
         except (NotImplementedError, AttributeError) as exc:
-            if plan_requires_backend:
+            if host_weight_plan is not None:
                 raise RuntimeError(
                     "The loader skipped ordinary weight materialization for DLO, "
                     "but the target device could not be resolved"

vllm_omni/diffusion/offloader/base.py

@@ -31,9 +31,6 @@ class OffloadConfig:
     # blocks from the loader-selected host backing with H2D only.
     dlo_use_allgather: bool = True
     dlo_resident_layers: int = 0  # leading DiT layers kept on device
-    # Optional per-worker ceiling for registering shared host weight cache mappings.
-    # Zero applies no additional ceiling; pin_cpu_memory controls registration.
-    dlo_host_weight_cache_pin_limit_gib: float = 0.0
 
     @classmethod
     def from_od_config(cls, od_config: OmniDiffusionConfig) -> "OffloadConfig":
@@ -65,7 +62,7 @@ class OffloadConfig:
         dp_size = 1
         if parallel_config is not None:
             dp_size = getattr(parallel_config, "data_parallel_size", 1)
-            # HSDP's fully_shard_degree also contributes to effective DP
+            # HSDP shard and replica sizes determine the effective group size.
             hsdp_shard_size = getattr(parallel_config, "hsdp_shard_size", -1) if use_hsdp else -1
             hsdp_replicate_size = getattr(parallel_config, "hsdp_replicate_size", 1) if use_hsdp else 1
             if use_hsdp and hsdp_shard_size > 0:
@@ -104,7 +101,6 @@ class OffloadConfig:
         # requirements (concurrent requests, dummy run skip).
         dlo_use_allgather = getattr(od_config, "dlo_use_allgather", True)
         dlo_resident_layers = int(getattr(od_config, "dlo_resident_layers", 0))
-        dlo_host_weight_cache_pin_limit_gib = float(getattr(od_config, "dlo_host_weight_cache_pin_limit_gib", 0.0))
         if dlo_resident_layers < 0:
             raise ValueError(f"dlo_resident_layers must be >= 0, got {dlo_resident_layers}")
         if dlo_resident_layers and dlo_use_allgather:
@@ -140,7 +136,6 @@ class OffloadConfig:
             dp_size=dp_size,
             dlo_use_allgather=dlo_use_allgather,
             dlo_resident_layers=dlo_resident_layers,
-            dlo_host_weight_cache_pin_limit_gib=dlo_host_weight_cache_pin_limit_gib,
         )
 
 

vllm_omni/diffusion/offloader/cuda_host_registration.py

@@ -1,280 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""CUDA implementation of read-only host-mapping registration."""
-
-from __future__ import annotations
-
-import ctypes
-import mmap
-from collections.abc import Mapping, Sequence
-from dataclasses import dataclass
-from typing import Protocol
-
-import torch
-
-from .host_registration import (
-    HostRegistrationBudgetError,
-    HostRegistrationCleanupError,
-    HostRegistrationError,
-)
-
-_CUDA_HOST_REGISTER_READ_ONLY = 0x08
-_CUDA_DEVICE_ATTRIBUTE_HOST_REGISTER_READ_ONLY_SUPPORTED = 113
-
-CudaHostRegistrationError = HostRegistrationError
-CudaHostRegistrationBudgetError = HostRegistrationBudgetError
-CudaHostRegistrationCleanupError = HostRegistrationCleanupError
-
-
-class _CudaRuntime(Protocol):
-    def cudaHostRegister(self, address: int, size: int, flags: int) -> int: ...
-
-    def cudaHostUnregister(self, address: int) -> int: ...
-
-    def cudaGetErrorString(self, error: int) -> str | bytes: ...
-
-
-@dataclass(frozen=True)
-class _AddressRange:
-    start: int
-    end: int
-
-    @property
-    def size(self) -> int:
-        return self.end - self.start
-
-
-def _coalesce_ranges(
-    ranges: Sequence[tuple[int, int]],
-    page_size: int = mmap.PAGESIZE,
-) -> tuple[_AddressRange, ...]:
-    """Page-align and merge overlapping ranges from one backing mapping."""
-    if page_size <= 0:
-        raise ValueError(f"page_size must be positive, got {page_size}")
-
-    aligned: list[_AddressRange] = []
-    for start, size in ranges:
-        if start <= 0 or size < 0:
-            raise ValueError(f"invalid host range start={start}, size={size}")
-        if size == 0:
-            continue
-        aligned_start = start - start % page_size
-        end = start + size
-        aligned_end = ((end + page_size - 1) // page_size) * page_size
-        aligned.append(_AddressRange(aligned_start, aligned_end))
-
-    merged: list[_AddressRange] = []
-    for region in sorted(aligned, key=lambda item: (item.start, item.end)):
-        if merged and region.start <= merged[-1].end:
-            previous = merged[-1]
-            merged[-1] = _AddressRange(previous.start, max(previous.end, region.end))
-        else:
-            merged.append(region)
-    return tuple(merged)
-
-
-def _tensor_regions(
-    sources_by_mapping: Mapping[str, Sequence[torch.Tensor]],
-) -> tuple[_AddressRange, ...]:
-    """Resolve storage spans without merging unrelated file mappings."""
-    regions: list[_AddressRange] = []
-    for mapping_name, tensors in sources_by_mapping.items():
-        ranges: list[tuple[int, int]] = []
-        for tensor in tensors:
-            if tensor.device.type != "cpu":
-                raise HostRegistrationError(
-                    f"CUDA host registration requires CPU storage, but {mapping_name!r} contains {tensor.device}"
-                )
-            storage = tensor.untyped_storage()
-            ranges.append((storage.data_ptr(), storage.nbytes()))
-        regions.extend(_coalesce_ranges(ranges))
-    return tuple(regions)
-
-
-def _error_message(runtime: _CudaRuntime, error: int) -> str:
-    try:
-        message = runtime.cudaGetErrorString(error)
-    except Exception:
-        return str(error)
-    if isinstance(message, bytes):
-        return message.decode(errors="replace")
-    return str(message)
-
-
-def _consume_last_cuda_error(expected_error: int) -> None:
-    """Clear a handled CUDA Runtime error before returning to PyTorch.
-
-    PyTorch's ``torch.cuda.cudart()`` binding exposes host registration but not
-    ``cudaGetLastError``. Resolve the already-loaded process symbol so this call
-    clears the same CUDA Runtime instance that produced the registration error.
-    A different pending error is not safe to hide behind the staging fallback.
-    """
-    try:
-        get_last_error = ctypes.CDLL(None).cudaGetLastError
-        get_last_error.argtypes = []
-        get_last_error.restype = ctypes.c_int
-        pending_error = int(get_last_error())
-    except (AttributeError, OSError) as exc:
-        raise HostRegistrationCleanupError("cannot clear CUDA's pending error after host-registration failure") from exc
-
-    if pending_error not in (0, expected_error):
-        raise HostRegistrationCleanupError(
-            f"host registration returned CUDA error {expected_error}, but cudaGetLastError reported {pending_error}"
-        )
-
-
-def _handled_error_message(runtime: _CudaRuntime, error: int) -> str:
-    """Format and consume one nonzero CUDA Runtime return code."""
-    error_code = int(error)
-    message = _error_message(runtime, error)
-    try:
-        _consume_last_cuda_error(error_code)
-    except HostRegistrationCleanupError as exc:
-        raise HostRegistrationCleanupError(f"{message}; {exc}") from exc
-    return message
-
-
-def _supports_read_only_host_registration(runtime: _CudaRuntime) -> bool:
-    """Query support required by immutable file-backed cache mappings."""
-    try:
-        get_attribute = getattr(runtime, "cudaDeviceGetAttribute", None)
-        if get_attribute is None:
-            get_attribute = ctypes.CDLL(None).cudaDeviceGetAttribute
-            get_attribute.argtypes = [
-                ctypes.POINTER(ctypes.c_int),
-                ctypes.c_int,
-                ctypes.c_int,
-            ]
-            get_attribute.restype = ctypes.c_int
-    except (AttributeError, OSError) as exc:
-        raise HostRegistrationError("cannot query CUDA read-only host-registration support") from exc
-
-    supported = ctypes.c_int()
-    try:
-        error = get_attribute(
-            ctypes.byref(supported),
-            _CUDA_DEVICE_ATTRIBUTE_HOST_REGISTER_READ_ONLY_SUPPORTED,
-            torch.accelerator.current_device_index(),
-        )
-    except Exception as exc:
-        raise HostRegistrationError(f"cannot query CUDA read-only host-registration support: {exc}") from exc
-    if int(error) != 0:
-        raise HostRegistrationError(
-            f"cudaDeviceGetAttribute(read-only host registration) failed: {_handled_error_message(runtime, error)}"
-        )
-    return bool(supported.value)
-
-
-class CudaHostRegistration:
-    """Own CUDA registrations for already-existing file-backed CPU tensors."""
-
-    def __init__(
-        self,
-        runtime: _CudaRuntime,
-        regions: tuple[_AddressRange, ...],
-    ) -> None:
-        self._runtime = runtime
-        self._regions = regions
-        self._closed = False
-
-    @classmethod
-    def create(
-        cls,
-        sources_by_mapping: Mapping[str, Sequence[torch.Tensor]],
-        *,
-        max_bytes: int | None,
-    ) -> CudaHostRegistration:
-        regions = _tensor_regions(sources_by_mapping)
-        total_bytes = sum(region.size for region in regions)
-        if max_bytes is not None and total_bytes > max_bytes:
-            raise HostRegistrationBudgetError(
-                f"mapped host ranges need {total_bytes} bytes, exceeding the {max_bytes}-byte registration budget"
-            )
-        if not regions:
-            raise HostRegistrationError("no non-empty host ranges were available for registration")
-        if not torch.cuda.is_available():
-            raise HostRegistrationError("CUDA is not available")
-
-        try:
-            runtime = torch.cuda.cudart()
-        except Exception as exc:
-            raise HostRegistrationError(f"cannot access the CUDA runtime: {exc}") from exc
-        if not _supports_read_only_host_registration(runtime):
-            raise HostRegistrationError(
-                "CUDA device does not support read-only host registration required by immutable "
-                "host weight cache mappings"
-            )
-
-        registered: list[_AddressRange] = []
-        try:
-            for region in regions:
-                error = runtime.cudaHostRegister(
-                    region.start,
-                    region.size,
-                    _CUDA_HOST_REGISTER_READ_ONLY,
-                )
-                if int(error) != 0:
-                    raise HostRegistrationError(
-                        "cudaHostRegister(read-only) failed for "
-                        f"[{region.start:#x}, {region.end:#x}): {_handled_error_message(runtime, error)}"
-                    )
-                registered.append(region)
-
-            unpinned = [
-                mapping_name
-                for mapping_name, tensors in sources_by_mapping.items()
-                if any(tensor.numel() and not tensor.is_pinned() for tensor in tensors)
-            ]
-            if unpinned:
-                raise HostRegistrationError(
-                    f"CUDA registration succeeded but PyTorch did not recognize pinned storage for {unpinned[:3]}"
-                )
-        except Exception as exc:
-            rollback_errors: list[str] = []
-            for region in reversed(registered):
-                try:
-                    error = runtime.cudaHostUnregister(region.start)
-                    if int(error) != 0:
-                        rollback_errors.append(
-                            f"cudaHostUnregister({region.start:#x}) failed: {_handled_error_message(runtime, error)}"
-                        )
-                except Exception as rollback_exc:
-                    rollback_errors.append(f"cudaHostUnregister({region.start:#x}) raised: {rollback_exc}")
-            if rollback_errors:
-                raise HostRegistrationCleanupError(
-                    f"CUDA host registration failed ({exc}); rollback errors: {rollback_errors[:3]}"
-                ) from exc
-            if isinstance(exc, HostRegistrationError):
-                raise
-            raise HostRegistrationError(f"CUDA host registration raised: {exc}") from exc
-
-        return cls(runtime, regions)
-
-    @property
-    def total_bytes(self) -> int:
-        return sum(region.size for region in self._regions)
-
-    @property
-    def region_count(self) -> int:
-        return len(self._regions)
-
-    def close(self) -> list[str]:
-        """Unregister every range, returning errors after best-effort cleanup."""
-        if self._closed:
-            return []
-        errors: list[str] = []
-        failed: list[_AddressRange] = []
-        for region in reversed(self._regions):
-            try:
-                error = self._runtime.cudaHostUnregister(region.start)
-                if int(error) != 0:
-                    errors.append(
-                        f"cudaHostUnregister({region.start:#x}) failed: {_handled_error_message(self._runtime, error)}"
-                    )
-                    failed.append(region)
-            except Exception as exc:
-                errors.append(f"cudaHostUnregister({region.start:#x}) raised: {exc}")
-                failed.append(region)
-        self._regions = tuple(reversed(failed))
-        self._closed = not self._regions
-        return errors

vllm_omni/diffusion/offloader/distributed_layerwise_backend.py

@@ -18,9 +18,7 @@ This module implements the RFC-1 "Distributed Layerwise Offload" mechanism that:
 
 from __future__ import annotations
 
-import time
 from itertools import chain
-from pathlib import Path
 from typing import Any
 
 import torch
@@ -29,7 +27,6 @@ from torch import nn
 from vllm.logger import init_logger
 
 from vllm_omni.diffusion.hooks import HookRegistry, ModelHook
-from vllm_omni.diffusion.model_loader.host_weight_cache_utils import file_digest
 from vllm_omni.diffusion.model_loader.host_weight_plan import (
     HostWeightPlan,
 )
@@ -37,12 +34,6 @@ from vllm_omni.platforms import current_omni_platform
 
 from .base import OffloadBackend, OffloadConfig
 from .block_discovery import get_blocks_from_dit
-from .host_registration import (
-    HostRegistration,
-    HostRegistrationCleanupError,
-    HostRegistrationError,
-    register_host_mappings,
-)
 from .module_collector import ModuleDiscovery
 from .offload_plan import (
     OffloadPlan,
@@ -102,7 +93,6 @@ class DistributedLayerwiseOffloadHook(ModelHook):
         self.rank = rank
         self.pin_memory = pin_memory
         self.rank_local_mmap = rank_local_mmap
-        self.registered_mmap = False
         self.tensor_transforms = tensor_transforms or {}
 
         self.copy_stream = copy_stream or current_omni_platform.Stream()
@@ -454,26 +444,6 @@ class DistributedLayerwiseOffloadHook(ModelHook):
     #  Prefetch: H2D + AllGather (overlapped on dedicated streams)      #
     # ------------------------------------------------------------------ #
 
-    @staticmethod
-    def _resolve_mmap_source(
-        source_info: dict[str, Any],
-        meta: dict[str, Any],
-        dtype: torch.dtype,
-    ) -> torch.Tensor:
-        source = source_info["tensor"]
-        transform = source_info["transform"]
-        if callable(transform):
-            source = transform(source)
-        if source.dtype != dtype or source.shape != meta["shape"] or source.stride() != meta["stride"]:
-            raise ValueError(
-                "mmap weight transform changed tensor layout for "
-                f"{source_info['name']!r}: expected dtype={dtype}, "
-                f"shape={tuple(meta['shape'])}, stride={meta['stride']}; "
-                f"got dtype={source.dtype}, shape={tuple(source.shape)}, "
-                f"stride={source.stride()}"
-            )
-        return source
-
     @staticmethod
     def _pack_mmap_sources(
         cpu_sources: dict[torch.dtype, list[dict[str, Any]]],
@@ -486,7 +456,18 @@ class DistributedLayerwiseOffloadHook(ModelHook):
             destination = slot_buffers[dtype][:total_numel]
             sources = cpu_sources[dtype]
             for source_info, meta in zip(sources, metas, strict=True):
-                source = DistributedLayerwiseOffloadHook._resolve_mmap_source(source_info, meta, dtype)
+                source = source_info["tensor"]
+                transform = source_info["transform"]
+                if callable(transform):
+                    source = transform(source)
+                if source.dtype != dtype or source.shape != meta["shape"] or source.stride() != meta["stride"]:
+                    raise ValueError(
+                        "mmap weight transform changed tensor layout for "
+                        f"{source_info['name']!r}: expected dtype={dtype}, "
+                        f"shape={tuple(meta['shape'])}, stride={meta['stride']}; "
+                        f"got dtype={source.dtype}, shape={tuple(source.shape)}, "
+                        f"stride={source.stride()}"
+                    )
                 start = meta["offset"]
                 physical_storage = destination[start : start + meta["numel"]]
                 if source.is_contiguous():
@@ -500,32 +481,6 @@ class DistributedLayerwiseOffloadHook(ModelHook):
             staged[dtype] = destination
         return staged
 
-    @staticmethod
-    def _copy_mmap_sources_to_device(
-        cpu_sources: dict[torch.dtype, list[dict[str, Any]]],
-        metadata: dict[torch.dtype, list[dict[str, Any]]],
-        gpu_buffers: dict[torch.dtype, torch.Tensor],
-        *,
-        non_blocking: bool,
-    ) -> None:
-        """Copy registered source views directly into flattened device buffers."""
-        for dtype, metas in metadata.items():
-            destination = gpu_buffers[dtype]
-            sources = cpu_sources[dtype]
-            for source_info, meta in zip(sources, metas, strict=True):
-                source = DistributedLayerwiseOffloadHook._resolve_mmap_source(source_info, meta, dtype)
-                start = meta["offset"]
-                physical_storage = destination[start : start + meta["numel"]]
-                async_copy = non_blocking and source.is_pinned()
-                if source.is_contiguous():
-                    physical_storage.copy_(source.flatten(), non_blocking=async_copy)
-                else:
-                    torch.as_strided(
-                        physical_storage,
-                        size=source.shape,
-                        stride=source.stride(),
-                    ).copy_(source, non_blocking=async_copy)
-
     def _stage_mmap_sources(self, slot: int) -> dict[torch.dtype, torch.Tensor]:
         """Pack this block's mmap views into one bounded host staging slot."""
         previous_copy = self.cpu_staging_events[slot]
@@ -559,27 +514,17 @@ class DistributedLayerwiseOffloadHook(ModelHook):
         assert gpu_weights is not None, f"gpu_buffers[{slot}] not allocated"
 
         if self.dp_size <= 1 or self.dp_group is None:
-            if self.rank_local_mmap and self.registered_mmap:
+            cpu_weights = self._stage_mmap_sources(slot) if self.rank_local_mmap else self.cpu_shards
-                with current_omni_platform.stream(self.copy_stream):
+            with current_omni_platform.stream(self.copy_stream):
-                    self._copy_mmap_sources_to_device(
+                for dtype, cpu_shard in cpu_weights.items():
-                        self.cpu_sources,
+                    gw = gpu_weights[dtype]
-                        self.metadata,
+                    async_copy = non_blocking and cpu_shard.is_pinned()
-                        gpu_weights,
+                    gw[: cpu_shard.numel()].copy_(cpu_shard, non_blocking=async_copy)
-                        non_blocking=non_blocking,
+                evt.record(self.copy_stream)
-                    )
+            if self.rank_local_mmap:
-                    evt.record(self.copy_stream)
+                # The CPU slot may be overwritten only after this H2D copy has
-            else:
+                # finished.  The shared event protects reuse by another hook.
-                cpu_weights = self._stage_mmap_sources(slot) if self.rank_local_mmap else self.cpu_shards
+                self.cpu_staging_events[slot] = evt
-                with current_omni_platform.stream(self.copy_stream):
-                    for dtype, cpu_shard in cpu_weights.items():
-                        gw = gpu_weights[dtype]
-                        async_copy = non_blocking and cpu_shard.is_pinned()
-                        gw[: cpu_shard.numel()].copy_(cpu_shard, non_blocking=async_copy)
-                    evt.record(self.copy_stream)
-                if self.rank_local_mmap:
-                    # The CPU slot may be overwritten only after this H2D copy has
-                    # finished.  The shared event protects reuse by another hook.
-                    self.cpu_staging_events[slot] = evt
         else:
             gpu_shards: dict[torch.dtype, torch.Tensor] = {}
             shard_bufs = self.gpu_shard_buffers[slot]
@@ -789,7 +734,6 @@ class PinnedResidentLayerGroup:
         self.copy_stream = copy_stream
         self.loaded = False
         self.rank_local_mmap = rank_local_mmap
-        self.registered_mmap = False
         self.pin_memory = pin_memory
         self._states: list[dict[str, Any]] = []
         self._gpu_buffers: list[dict[torch.dtype, torch.Tensor]] = []
@@ -860,14 +804,6 @@ class PinnedResidentLayerGroup:
         ready = current_omni_platform.Event()
         with current_omni_platform.stream(self.copy_stream):
             for index, (state, block_buffers) in enumerate(zip(self._states, gpu_buffers)):
-                if self.rank_local_mmap and self.registered_mmap:
-                    DistributedLayerwiseOffloadHook._copy_mmap_sources_to_device(
-                        state["cpu_sources"],
-                        state["metadata"],
-                        block_buffers,
-                        non_blocking=True,
-                    )
-                    continue
                 if self.rank_local_mmap:
                     slot = index % 2
                     previous_copy = self._cpu_staging_events[slot]
@@ -965,11 +901,8 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
         self._resident_layer_group: PinnedResidentLayerGroup | None = None
         self._using_mmap = False
         self._using_rank_local_mmap = False
-        self._using_registered_mmap = False
         self.host_weight_plan = host_weight_plan
         self._mmap_transforms_by_tensor_id: dict[int, Any] = {}
-        self._host_weight_cache_mapped_sources: dict[str, list[torch.Tensor]] = {}
-        self._host_registration: HostRegistration | None = None
 
     def load_resident_layers(self) -> None:
         """Load the model-declared leading blocks for the denoise stage."""
@@ -1090,7 +1023,7 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
                     framework="pt",
                     device="cpu",
                 )
-            tensor = file_cache[binding.file_path].get_tensor(binding.storage_key)
+            tensor = file_cache[binding.file_path].get_tensor(binding.checkpoint_key)
             if is_parameter:
                 replacement = torch.nn.Parameter(tensor, requires_grad=target.requires_grad)
                 parent._parameters[leaf_name] = replacement
@@ -1101,6 +1034,9 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
 
         logger.info("Realized %d loader-planned tensors as mmap views", len(loaded_names))
 
+        if getattr(pipeline, "_dlo_fp8_cache_normalized", False):
+            self._process_normalized_fp8_cache(pipeline)
+
         # Keep file handles open — _shard_and_pin will read from the mmap views.
         # They will be released after _shard_and_pin completes (when params are
         # replaced with offload placeholders).
@@ -1170,178 +1106,51 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
                 f"(first 5: {remaining_meta[:5]})."
             )
 
-    def _load_weights_from_host_weight_cache(
+    @staticmethod
-        self,
+    def _process_normalized_fp8_cache(pipeline: nn.Module) -> None:
-        pipeline: nn.Module,
+        """Finalize serialized FP8 parameters after mmap replacement.
-        modules,
-        plan: HostWeightPlan,
-    ) -> None:
-        """Replace final ordinary-loader tensors with validated cache views."""
-        from safetensors import safe_open
 
-        if any(binding.transform is not None for binding in plan.bindings.values()):
+        Phase-I cache files use the serialized FP8 checkpoint layout: the
-            raise RuntimeError("Host weight cache bindings cannot carry deferred checkpoint transforms")
+        weight is stored before the quantization method's final transpose, and
+        ``weight_scale`` is stored as an explicit cache tensor.  The ordinary
+        loader normally runs this method before DLO owns the plan; mmap loading
+        must run the same finalizer after replacing meta parameters.
+        """
+        from vllm.model_executor.layers.quantization.fp8 import Fp8LinearMethod
+        from vllm.model_executor.utils import replace_parameter
 
-        cache_files = {binding.file_path for binding in plan.bindings.values()}
+        for module in pipeline.modules():
-        if plan.expected_file_digests is None or set(plan.expected_file_digests) != cache_files:
+            quant_method = getattr(module, "quant_method", None)
-            raise RuntimeError("Host weight cache plan does not cover every shard digest")
+            if not isinstance(quant_method, Fp8LinearMethod):
-        for file_path in sorted(cache_files):
+                continue
-            try:
+            if getattr(module, "_already_called_process_weights_after_loading", False):
-                actual_digest = file_digest(Path(file_path))
+                continue
-            except OSError as exc:
-                raise RuntimeError(f"Cannot revalidate host weight cache shard {file_path}: {exc}") from exc
-            if actual_digest != plan.expected_file_digests[file_path]:
-                raise RuntimeError(f"Host weight cache shard changed after loader validation: {file_path}")
 
-        required_names: set[str] = set()
+            # ``Fp8LinearMethod.process_weights_after_loading`` also handles
-        for dit_name, dit_module in zip(modules.dit_names, modules.dits):
+            # non-serialized checkpoints.  Its common path calls
-            required_names.update(f"{dit_name}.{name}" for name, _ in dit_module.named_parameters())
+            # ``process_fp8_weight_tensor_strategy`` to requantize fused
-            for name, _ in dit_module.named_buffers():
+            # tensors, which is a CUDA-only operation.  A normalized cache
-                parent_path, _, leaf_name = name.rpartition(".")
+            # already contains the serialized FP8 values and the generated
-                owner = dit_module.get_submodule(parent_path)
+            # scales, so only perform the layout transition and the kernel's
-                if leaf_name not in owner._non_persistent_buffers_set:
+            # own preprocessing here.  This keeps the warm path CPU/mmap
-                    required_names.add(f"{dit_name}.{name}")
+            # compatible and preserves the cache bytes exactly.
-        if required_names != set(plan.bindings):
+            if quant_method.block_quant:
-            missing = sorted(required_names - set(plan.bindings))
+                raise ValueError("Phase-I normalized FP8 cache does not support block-quantized weights")
-            unexpected = sorted(set(plan.bindings) - required_names)
+            replace_parameter(module, "weight", module.weight.t().data)
-            raise RuntimeError(
+            # The online quantizer records one scalar.  A serialized FP8
-                "Host weight cache plan coverage changed after loader validation: "
+            # checkpoint may expose one repeated slot per logical shard in a
-                f"missing={missing[:5]}, unexpected={unexpected[:5]}"
+            # fused module, but those slots must agree before they can be
-            )
+            # collapsed to the canonical per-tensor ``[1, 1]`` form.
-
+            weight_scale = module.weight_scale.reshape(-1)
-        file_cache: dict[str, Any] = {}
+            if weight_scale.numel() > 1 and not torch.all(weight_scale == weight_scale[0]):
-        replacements: list[tuple[torch.Tensor, torch.Tensor]] = []
+                raise ValueError("Phase-I normalized FP8 cache contains non-uniform fused weight scales")
-        mapped_sources: dict[str, list[torch.Tensor]] = {}
+            weight_scale = weight_scale[:1].reshape(1, 1)
-        try:
+            replace_parameter(module, "weight_scale", weight_scale.data)
-            # Validate and map every tensor before mutating the pipeline. A
+            module.input_scale = None
-            # missing/corrupt shard therefore leaves the ordinary tensors intact.
+            if quant_method.use_marlin and hasattr(quant_method.fp8_linear, "marlin_input_dtype"):
-            for runtime_name, binding in plan.bindings.items():
+                quant_method.fp8_linear.marlin_input_dtype = quant_method.marlin_input_dtype
-                parent_path, _, leaf_name = runtime_name.rpartition(".")
+            quant_method.fp8_linear.process_weights_after_loading(module)
-                try:
+            module._already_called_process_weights_after_loading = True
-                    parent = pipeline.get_submodule(parent_path)
-                except AttributeError as exc:
-                    raise RuntimeError(f"Host weight cache target module {parent_path!r} no longer exists") from exc
-
-                target = parent._parameters.get(leaf_name)
-                if target is None:
-                    target = parent._buffers.get(leaf_name)
-                if target is None:
-                    raise RuntimeError(f"Host weight cache target tensor {runtime_name!r} no longer exists")
-                if target.device.type != "cpu" or target.is_meta:
-                    raise RuntimeError(
-                        f"Host weight cache target {runtime_name!r} is not an ordinary CPU tensor: {target.device}"
-                    )
-
-                if binding.file_path not in file_cache:
-                    file_cache[binding.file_path] = safe_open(
-                        binding.file_path,
-                        framework="pt",
-                        device="cpu",
-                    )
-                tensor = file_cache[binding.file_path].get_tensor(binding.storage_key)
-                if tensor.shape != target.shape or tensor.dtype != target.dtype:
-                    raise RuntimeError(
-                        f"Host weight cache metadata changed for {runtime_name!r}: "
-                        f"cache={tuple(tensor.shape)}/{tensor.dtype}, "
-                        f"runtime={tuple(target.shape)}/{target.dtype}"
-                    )
-                replacements.append((target, tensor))
-                mapped_sources.setdefault(binding.file_path, []).append(tensor)
-        except Exception:
-            file_cache.clear()
-            raise
-
-        # Preserve Parameter objects and their loader/runtime attributes; only
-        # their storage changes. This also keeps any references held by model
-        # code valid while dropping the private allocator-backed storage.
-        for target, tensor in replacements:
-            set_tensor_storage(target, tensor)
-
-        self._mmap_transforms_by_tensor_id.clear()
-        self._mmap_file_cache = file_cache
-        self._host_weight_cache_mapped_sources = mapped_sources
-        logger.info(
-            "Remapped %d final DiT tensors from host weight cache %s",
-            len(replacements),
-            plan.runtime_layout_key[:12] if plan.runtime_layout_key else "unknown",
-        )
-
-    def _try_register_host_weight_cache_mmap(self) -> bool:
-        """Register complete cache mappings under the pinned-memory policy."""
-        pin_limit_gib = self.config.dlo_host_weight_cache_pin_limit_gib
-        if not self.config.pin_cpu_memory:
-            logger.info("DLO host weight cache registration disabled by pin_cpu_memory=False; using host staging")
-            return False
-        if not self._host_weight_cache_mapped_sources:
-            logger.warning("DLO host weight cache registration found no mapped sources; using bounded host staging")
-            return False
-
-        max_bytes = int(pin_limit_gib * 1024**3) if pin_limit_gib > 0 else None
-        started = time.perf_counter()
-        try:
-            registration = register_host_mappings(
-                self._host_weight_cache_mapped_sources,
-                device=self.device,
-                max_bytes=max_bytes,
-            )
-        except HostRegistrationCleanupError:
-            # Falling back could unmap a range the platform still owns. Abort
-            # the worker so process/context teardown provides final cleanup.
-            logger.exception("DLO host weight cache registration rollback failed")
-            raise
-        except HostRegistrationError as exc:
-            logger.warning(
-                "DLO host weight cache direct H2D unavailable (%s); using bounded host staging",
-                exc,
-            )
-            return False
-
-        self._host_registration = registration
-        logger.info(
-            "Registered %.2f GiB of shared host weight cache mappings in %d range(s) for direct H2D in %.3f s",
-            registration.total_bytes / 1024**3,
-            registration.region_count,
-            time.perf_counter() - started,
-        )
-        return True
-
-    def _configure_host_weight_cache_transfer(
-        self,
-        hooks: list[DistributedLayerwiseOffloadHook],
-    ) -> None:
-        """Select registered mmap or staging once for every cache consumer."""
-        plan = self.host_weight_plan
-        if (
-            (not hooks and self._resident_layer_group is None)
-            or not self._using_rank_local_mmap
-            or plan is None
-            or plan.backing_kind != "host_weight_cache"
-        ):
-            return
-
-        self._using_registered_mmap = self._try_register_host_weight_cache_mmap()
-        self._host_weight_cache_mapped_sources.clear()
-        for hook in hooks:
-            hook.registered_mmap = self._using_registered_mmap
-        if self._resident_layer_group is not None:
-            self._resident_layer_group.registered_mmap = self._using_registered_mmap
-            if self._using_registered_mmap:
-                self._resident_layer_group._cpu_staging_buffers.clear()
-
-    def _release_registered_mmap(self) -> bool:
-        registration = self._host_registration
-        if registration is None:
-            return True
-        errors = registration.close()
-        if errors:
-            logger.error(
-                "DLO host weight cache unregistration failed; retaining mmap handles for retry/process exit: %s",
-                errors[:3],
-            )
-            return False
-        self._host_registration = None
-        logger.info("Unregistered DLO host weight cache mappings")
-        return True
 
     def _init_dp_group(self) -> None:
         """Reuse the process group initialized by parallel_state.
@@ -1683,41 +1492,17 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
         self._using_mmap = self.host_weight_plan is not None
         self._using_rank_local_mmap = self._using_mmap and self.dp_size <= 1
         if self._using_mmap:
-            if self.host_weight_plan.backing_kind == "checkpoint_mmap":
+            if self.host_weight_plan.backing_kind != "checkpoint_mmap":
-                self._load_weights_via_mmap(
-                    pipeline,
-                    modules,
-                    self.host_weight_plan,
-                )
-            elif self.host_weight_plan.backing_kind == "host_weight_cache":
-                if self.config.dlo_use_allgather:
-                    raise ValueError("Host weight cache backing is no-AllGather only in the first implementation")
-                self._using_rank_local_mmap = True
-                try:
-                    self._load_weights_from_host_weight_cache(
-                        pipeline,
-                        modules,
-                        self.host_weight_plan,
-                    )
-                except Exception as exc:
-                    logger.warning(
-                        "DLO host weight cache remapping failed; retaining ordinary host tensors: %s",
-                        exc,
-                    )
-                    self.host_weight_plan = None
-                    self._using_mmap = False
-                    self._using_rank_local_mmap = False
-                else:
-                    # Assignments above dropped the model's private tensor
-                    # references. Reclaim those allocations before hook setup
-                    # so steady-state PSS reflects the shared mmap backing.
-                    self._cleanup_after_loading()
-            else:
                 raise ValueError(f"Unsupported DLO host-weight backing: {self.host_weight_plan.backing_kind}")
+            self._load_weights_via_mmap(
+                pipeline,
+                modules,
+                self.host_weight_plan,
+            )
             if self._using_rank_local_mmap:
                 logger.info(
-                    "DLO rank-local mmap storage enabled: file-backed pages are node-shared; "
+                    "DLO rank-local mmap storage enabled: checkpoint pages are "
-                    "transfer setup will select registered direct H2D or bounded host staging"
+                    "node-shared; each worker owns only two bounded host staging slots"
                 )
         else:
             remaining_meta = [
@@ -1887,9 +1672,6 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
             )
             pipeline._dlo_residency_controller = self
 
-        all_hooks = [hook for group in self._all_hook_groups for hook in group]
-        self._configure_host_weight_cache_transfer(all_hooks)
-
         if not self._all_hook_groups:
             self.enabled = bool(self._resident_blocks)
             if self._using_mmap and not self.enabled:
@@ -1899,14 +1681,17 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
         # Unified allocation: 2 shared output buffers + 2 shared shard buffers
         # sized to the max block across ALL module groups (gen_layers +
         # language_model).  Groups execute sequentially, so 2 buffers suffice.
+        all_hooks: list[DistributedLayerwiseOffloadHook] = []
+        for group in self._all_hook_groups:
+            all_hooks.extend(group)
+
         unified_buffers = self._allocate_shared_buffers(all_hooks)
         unified_shard_buffers = None
         if self.dp_size > 1:
             unified_shard_buffers = self._allocate_shared_shard_buffers(all_hooks)
-
         unified_cpu_staging = None
         cpu_staging_events = None
-        if self._using_rank_local_mmap and not self._using_registered_mmap:
+        if self._using_rank_local_mmap:
             unified_cpu_staging = self._allocate_shared_cpu_staging_buffers(
                 all_hooks,
                 self._resident_layer_group,
@@ -1988,7 +1773,6 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
     def _release_mmap_handles(self) -> None:
         """Release safetensors mmap file handles."""
         self._mmap_transforms_by_tensor_id.clear()
-        self._host_weight_cache_mapped_sources.clear()
         if hasattr(self, "_mmap_file_cache"):
             self._mmap_file_cache.clear()
             del self._mmap_file_cache
@@ -2011,11 +1795,8 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
         if not self.enabled and not hasattr(self, "_mmap_file_cache"):
             return
 
-        # Copy and communication streams are asynchronous in both AllGather
+        if self._using_rank_local_mmap:
-        # and no-AllGather modes. Drain all device work before hooks, process
+            current_omni_platform.synchronize()
-        # groups, registrations, or mapped storage can be torn down.
-        current_omni_platform.synchronize()
-        registration_released = self._release_registered_mmap()
 
         for blocks in self._blocks:
             for block in blocks:
@@ -2031,11 +1812,9 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
         self._all_hook_groups.clear()
         self._resident_blocks.clear()
         self._resident_layer_group = None
-        if registration_released:
+        self._release_mmap_handles()
-            self._release_mmap_handles()
         self._using_mmap = False
         self._using_rank_local_mmap = False
-        self._using_registered_mmap = False
         self.enabled = False
         logger.info("Distributed layer-wise offloading disabled")
 

vllm_omni/diffusion/offloader/host_registration.py

@@ -1,62 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Platform-neutral contract for registering existing host mappings."""
-
-from __future__ import annotations
-
-from collections.abc import Mapping, Sequence
-from typing import Protocol
-
-import torch
-
-
-class HostRegistrationError(RuntimeError):
-    """Raised when existing host ranges cannot be registered safely."""
-
-
-class HostRegistrationBudgetError(HostRegistrationError):
-    """Raised before mutation when the complete mapping exceeds its budget."""
-
-
-class HostRegistrationCleanupError(HostRegistrationError):
-    """Raised when partial registration cannot be rolled back safely."""
-
-
-class HostRegistration(Protocol):
-    """Lifecycle owned by DLO for one platform's registered host ranges."""
-
-    @property
-    def total_bytes(self) -> int: ...
-
-    @property
-    def region_count(self) -> int: ...
-
-    def close(self) -> list[str]: ...
-
-
-def register_host_mappings(
-    sources_by_mapping: Mapping[str, Sequence[torch.Tensor]],
-    *,
-    device: torch.device,
-    max_bytes: int | None,
-) -> HostRegistration:
-    """Register mappings with the active platform or report unsupported use.
-
-    CUDA is the first implementation. Other platforms retain DLO's bounded
-    host-staging path until they provide an equivalent registration backend.
-    ``max_bytes=None`` applies no additional ceiling to the complete mapping.
-    """
-    if device.type == "cuda":
-        from .cuda_host_registration import CudaHostRegistration
-
-        return CudaHostRegistration.create(sources_by_mapping, max_bytes=max_bytes)
-    raise HostRegistrationError(f"host-mapping registration is not supported on {device.type}")
-
-
-__all__ = [
-    "HostRegistration",
-    "HostRegistrationBudgetError",
-    "HostRegistrationCleanupError",
-    "HostRegistrationError",
-    "register_host_mappings",
-]

vllm_omni/diffusion/worker/diffusion_worker.py

@@ -279,9 +279,8 @@ class DiffusionWorker:
                 allgather_degree=parallel_config.allgather_degree,
                 tensor_parallel_size=parallel_config.tensor_parallel_size,
                 pipeline_parallel_size=parallel_config.pipeline_parallel_size,
-                fully_shard_degree=parallel_config.hsdp_shard_size if parallel_config.use_hsdp else 1,
-                hsdp_replicate_size=parallel_config.hsdp_replicate_size if parallel_config.use_hsdp else 1,
                 enable_expert_parallel=parallel_config.enable_expert_parallel,
+                use_hsdp=parallel_config.use_hsdp,
             )
             if (
                 getattr(self.od_config, "diffusion_kv_mode", DiffusionKVCacheMode.DENSE_LEGACY)
@@ -859,18 +858,11 @@ class DiffusionWorker:
 
     def shutdown(self) -> None:
         """Shutdown the worker and cleanup distributed environment."""
-        try:
+        if self.model_runner is not None:
-            if self.model_runner is not None:
+            mgr = getattr(self.model_runner, "kv_transfer_manager", None)
-                mgr = getattr(self.model_runner, "kv_transfer_manager", None)
+            if mgr is not None:
-                try:
+                mgr.shutdown_prefetch()
-                    offload_backend = getattr(self.model_runner, "offload_backend", None)
+        destroy_distributed_env()
-                    if offload_backend is not None:
-                        offload_backend.disable()
-                finally:
-                    if mgr is not None:
-                        mgr.shutdown_prefetch()
-        finally:
-            destroy_distributed_env()
 
 
 class CustomPipelineWorkerExtension:

vllm_omni/engine/arg_utils.py

@@ -545,11 +545,8 @@ class OrchestratorArgs:
     enable_layerwise_offload: bool = False
     enable_distributed_layerwise_offload: bool = False
     dlo_use_allgather: bool = True
-    dlo_use_host_weight_cache: bool = False
-    dlo_host_weight_cache_dir: str | None = None
-    dlo_host_weight_cache_lock_timeout: float = 600.0
-    dlo_host_weight_cache_pin_limit_gib: float = 0.0
     dlo_resident_layers: int = 0
+    dlo_fp8_cache_dir: str | None = None
     boundary_ratio: float | None = None
     flow_shift: float | None = None
     diffusion_kv_cache_dtype: str | None = None

vllm_omni/engine/async_omni_engine.py

@@ -971,7 +971,7 @@ class AsyncOmniEngine:
             ulysses_mode = normalized_kwargs.get("ulysses_mode") or "strict"
             sequence_parallel_size = normalized_kwargs.get("sequence_parallel_size")
             pipeline_parallel_size = normalized_kwargs.get("pipeline_parallel_size") or 1
-            data_parallel_size = normalized_kwargs.get("data_parallel_size") or 1
+            data_parallel_size = normalized_kwargs.get("data_parallel_size")
             tensor_parallel_size = normalized_kwargs.get("tensor_parallel_size") or 1
             cfg_parallel_size = normalized_kwargs.get("cfg_parallel_size") or 1
             pipeline_parallel_size = normalized_kwargs.get("pipeline_parallel_size") or 1
@@ -1004,6 +1004,11 @@ class AsyncOmniEngine:
                 hsdp_replicate_size=hsdp_replicate_size,
             )
 
+        num_gpus = normalized_kwargs.get("num_gpus")
+        if num_gpus is not None:
+            num_gpus = int(num_gpus)
+            parallel_config.resolve_data_parallel_size(num_gpus)
+
         num_devices = max(1, int(parallel_config.world_size))
         devices = ",".join(str(i) for i in range(num_devices))
         model_class_name = kwargs.get("model_class_name", None)
@@ -1040,11 +1045,8 @@ class AsyncOmniEngine:
             "enable_layerwise_offload": kwargs.get("enable_layerwise_offload", False),
             "enable_distributed_layerwise_offload": kwargs.get("enable_distributed_layerwise_offload", False),
             "dlo_use_allgather": kwargs.get("dlo_use_allgather", True),
-            "dlo_use_host_weight_cache": kwargs.get("dlo_use_host_weight_cache", False),
-            "dlo_host_weight_cache_dir": kwargs.get("dlo_host_weight_cache_dir", None),
-            "dlo_host_weight_cache_lock_timeout": kwargs.get("dlo_host_weight_cache_lock_timeout", 600.0),
-            "dlo_host_weight_cache_pin_limit_gib": kwargs.get("dlo_host_weight_cache_pin_limit_gib", 0.0),
             "dlo_resident_layers": kwargs.get("dlo_resident_layers", 0),
+            "dlo_fp8_cache_dir": kwargs.get("dlo_fp8_cache_dir", None),
             "enforce_eager": False if kwargs.get("enforce_eager") is None else kwargs.get("enforce_eager"),
             "diffusion_compile_granularity": (
                 "regional"
@@ -1096,6 +1098,8 @@ class AsyncOmniEngine:
                 else {}
             ),
         }
+        if num_gpus is not None:
+            stage_engine_args["num_gpus"] = num_gpus
         # Only set dtype if it was already explicitly passed and normalized
         if "dtype" in normalized_kwargs:
             stage_engine_args["dtype"] = normalized_kwargs["dtype"]

vllm_omni/entrypoints/cli/serve.py

@@ -722,25 +722,6 @@ class OmniServeCommand(CLISubcommand):
                 "independently (including existing TP shards)."
             ),
         )
-        omni_config_group.add_argument(
-            "--dlo-use-host-weight-cache",
-            action="store_true",
-            help=(
-                "Use the node-local final-layout host weight cache for no-AllGather DLO. "
-                "With the default pinned-memory policy, supported platforms register the immutable mapping "
-                "for direct H2D; otherwise DLO uses bounded host staging."
-            ),
-        )
-        omni_config_group.add_argument(
-            "--dlo-host-weight-cache-pin-limit-gib",
-            type=float,
-            default=0.0,
-            help=(
-                "Optional maximum GiB of read-only host weight cache mappings each worker may "
-                "register for direct H2D. Zero applies no additional registration ceiling; "
-                "this option requires --dlo-use-host-weight-cache."
-            ),
-        )
         omni_config_group.add_argument(
             "--dlo-resident-layers",
             type=int,
@@ -748,6 +729,14 @@ class OmniServeCommand(CLISubcommand):
             help="Keep this many leading main-DiT blocks resident on the device "
             "while distributed layerwise offload streams the remaining blocks.",
         )
+        omni_config_group.add_argument(
+            "--dlo-fp8-cache-dir",
+            type=str,
+            default=None,
+            help="Optional Phase-I normalized online-FP8 cache directory. "
+            "A cache miss builds the cache after the ordinary loader; a valid "
+            "cache hit uses serialized FP8 checkpoint mmap with DLO.",
+        )
         # Video model parameters (e.g., Wan2.2) - engine-level
         omni_config_group.add_argument(
             "--boundary-ratio",