[Diffusion] Simplify DLO host plan setup

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
[Diffusion] Prefer host-cache registration when pinned
2026-08-19 23:00:31 +08:00 · 2026-08-19 18:13:44 +08:00 · 2026-08-19 11:23:36 +08:00 · 2026-08-19 11:23:36 +08:00 · 2026-08-19 11:23:36 +08:00 · 2026-08-19 11:23:36 +08:00
27 changed files with 4021 additions and 288 deletions
@@ -43,21 +43,291 @@ 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. Consequently, no-AllGather replicas on
-the same node can share immutable checkpoint pages when direct mmap is
-selected, while the ordinary-loader fallback still keeps a private runtime
-copy per process.
+otherwise it uses the ordinary loader. In no-AllGather mode, the Phase B host
+weight cache can explicitly normalize supported final ordinary-loader DiT
+tensors into an immutable node-local mmap entry. Ordinary pinned weights remain
+the default after checkpoint mmap fallback when the cache is not selected; cache
+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, but those configurations do not use checkpoint mmap and must not be
-used to claim shared-mmap host-memory savings.
+tensors. Direct checkpoint mmap still does not cover TP, but the Phase B cache
+can share each final TP coordinate among equivalent processes.

-The compatibility matrix below describes the current implementation. The
-unit-level guards are covered, but not every parallelism combination has a
-full model-and-hardware end-to-end test.
+The compatibility matrix below defines the current implementation boundary.
+Hardware and performance qualification is tracked separately from this
+long-lived design contract.

-## Design
+## Architecture
+
+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

@@ -65,15 +335,10 @@ 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.

-The DLO weight-sharding group is selected as follows:
-
-1. Use the existing DP group when `data_parallel_size > 1`.
-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.
+AllGather mode selects one existing group for DLO weight sharding; no-AllGather
+mode creates no DLO group. Neither mode changes ordinary TP or SP model
+collectives. The mode-specific group and scheduling contracts are described
+below.

 ### The loader owns host-weight planning

@@ -105,7 +370,24 @@ 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 path
+## AllGather design
+
+### Group selection
+
+The DLO weight-sharding group is selected in this order:
+
+1. Use the existing DP group when `data_parallel_size > 1`.
+2. When DP is one and SP is greater than one, use the existing SP group.
+3. Otherwise, use an effective group size of one and issue no collective.
+
+TP is deliberately not used as DLO's AllGather group. HSDP has its own
+parameter-sharding lifecycle and cannot be sharded a second time by DLO.
+
+### Sharded transfer and double buffering
+
+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
@@ -113,6 +395,15 @@ 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]
@@ -138,11 +429,29 @@ 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.

-### Rank-local path without DLO AllGather
+Fast NVLink/NVSwitch changes collective cost, not this execution contract. For
+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
-either a loader-approved checkpoint mapping or ordinary runtime tensors.
+a loader-approved checkpoint mapping, a normalized host weight cache mapping, or
+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
@@ -151,10 +460,17 @@ 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.

-When direct mmap preflight fails, the regular model loader remains responsible
-for preparing each rank's weights, including TP-local tensors or HSDP-managed
-parameters. In that fallback, each pure-DP process keeps a private full runtime
-copy.
+For a host weight cache mapping, the default pinned-memory policy attempts to
+replace those host slots with direct copies from the shared final tensor views.
+Disabling pinned memory or a failed registration retains staging behavior.
+
+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:

@@ -165,18 +481,109 @@ 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 ordinary-loader fallback keeps one private runtime
-  copy per rank.
+  mmap is selected. The host weight cache can share equivalent ordinary-loader
+  outputs; its fallback keeps one private runtime 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 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, but DLO keeps standard-loader rank-local weights and adds no SP weight collective. |
-| **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 by configuration. HSDP owns parameter sharding and its own gathers; DLO only stages rank-local parameters. End-to-end coverage is limited. |
+| **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. |
+| **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. |
+| **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. |
+| **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. |

 ### Combined dimensions

@@ -192,7 +599,9 @@ This mode means:
 - **HSDP + DP or TP:** rejected independently by the diffusion parallel
  configuration.

-## Request and loading constraints
+## Mode constraints
+
+### AllGather constraints

 AllGather DP multi-concurrency requires:

@@ -200,109 +609,54 @@ 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.
+requirements. Host weight cache v1 is no-AllGather only and rejects all
+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. 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.
+ordinary loader.

-A normalized runtime mmap cache, built through the ordinary loader, is the
-proposed general mechanism for sharing transformed TP or quantized layouts.
-That cache and its publication/lifecycle protocol are intentionally outside
-this phase; see [RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195).
+## Validation status

-## Validation coverage
-
-Current source-level validation includes:
-
- 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.
+Historical test counts and hardware measurements are intentionally not kept
+in this design document because they can drift as the implementation changes.
+Revalidation must run against the final commit and publish commit-pinned
+artifacts before making hardware or performance claims. Track that work in
+[issue #6231](https://github.com/vllm-project/vllm-omni/issues/6231).

 ## Recommendations

- Use **DP + DLO AllGather** for the supported throughput and host-memory
-  scaling path.
+### AllGather
+
+- 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; other layouts
-  retain the ordinary loader's private host memory behavior and are outside
-  the Phase A shared-mmap support scope.
- Prefer **HSDP alone** for production HSDP deployments until the combined
-  HSDP + DLO no-AllGather path has broader end-to-end coverage.
+  direct-mmap deployments can share checkpoint pages per node. Enable the
+  host weight cache only when independent replicas have repeated final layouts
+  and benchmark the target CPU/memory topology, especially for matching
+  coordinates across multiple TP engines. With the default pinned-memory
+  policy, supported CUDA workers prefer registered direct H2D. Configure a
+  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.
@@ -0,0 +1,165 @@
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+  <title id="title">AllGather DLO sharded host weights and lockstep device reconstruction</title>
+  <desc id="desc">An external orchestrator submits one synchronized wave to a DP2 by TP2 diffusion engine. Each DP replica contains TP0 and TP1 workers with private pinned host shards and two rotating GPU weight slots. DLO AllGather groups connect matching TP coordinates across DP replicas, reconstructing full rank-local blocks on each device in lockstep. Ordinary TP groups remain within each DP replica.</desc>
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+  <text x="40" y="74" class="subtitle">DP2 &#215; TP2 shown: DLO groups span DP replicas per TP coordinate; ordinary TP stays within each replica</text>
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+  <rect x="390" y="94" width="500" height="68" rx="10" class="router"/>
+  <text x="640" y="121" text-anchor="middle" class="section">External router / orchestrator</text>
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+
+  <rect x="30" y="184" width="1220" height="634" rx="14" class="node"/>
+  <text x="52" y="211" class="eyebrow">ONE NODE &#183; ONE SYNCHRONIZED ENGINE, PROCESS-OWNED HOST SHARDS</text>
+
+  <path d="M 640 162 L 640 232" class="request-path"/>
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+
+  <rect x="82" y="332" width="473" height="64" rx="8" class="gpu"/>
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+  <text x="316" y="357" text-anchor="middle" class="slot-title">SLOT 0 &#183; CURRENT</text>
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+
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+
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+  <text x="395" y="545" text-anchor="middle" font-family="system-ui, sans-serif" font-size="15" font-weight="700" fill="#ffffff">&#8644;</text>
+  <text x="420" y="536" class="lock-title">DLO AllGather requires lockstep block order</text>
+  <text x="420" y="553" class="lock-text">every participant enters each block collective in the same sequence</text>
+
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+  <text x="276" y="618" text-anchor="middle" class="h2d-label">local shard H2D &#215;2</text>
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+  <text x="1004" y="618" text-anchor="middle" class="h2d-label">local shard H2D &#215;2</text>
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+  <text x="78" y="676" class="eyebrow">GROUP TOPOLOGY</text>
+  <text x="78" y="702" class="box-title">DP2 &#215; TP2</text>
+  <text x="78" y="725" class="box-text">DLO groups: same TP coordinate</text>
+  <text x="78" y="746" class="box-text">across DP replicas</text>
+  <text x="78" y="767" class="box-text">TP stays the model-parallel group.</text>
+
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+  <text x="500" y="716" text-anchor="middle" class="shard-title">DP replica 0 &#183; private</text>
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+  <text x="640" y="773" text-anchor="middle" class="box-text">Across each DLO group, shards total approximately one rank-local runtime block copy</text>
+
+  <rect x="980" y="650" width="242" height="140" rx="10" class="scope"/>
+  <text x="1000" y="676" class="eyebrow">HOT PATH &#183; PER WORKER</text>
+  <text x="1000" y="702" class="box-title">Prefetch next block</text>
+  <text x="1000" y="725" class="box-text">local pinned shard &#8594; GPU</text>
+  <text x="1000" y="746" class="box-text">DLO AllGather &#8594; full block</text>
+  <text x="1000" y="767" class="box-text">rotate slots; overlap compute</text>
+</svg>
@@ -0,0 +1,202 @@
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+  <title id="title">No-AllGather DLO shared host weights and independent runtime orchestration</title>
+  <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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+    <marker id="runtime-arrow" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="7" markerHeight="7" orient="auto">
+      <path d="M 0 0 L 10 5 L 0 10 z" fill="#2563eb"/>
+    </marker>
+    <style>
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+      .node { fill: #f8fafc; stroke: #cbd5e1; }
+      .engine { fill: #ffffff; stroke: #cbd5e1; }
+      .router { fill: #f5f3ff; stroke: #c4b5fd; }
+      .scope { fill: #f8fafc; stroke: #cbd5e1; }
+      .cache { fill: #fff7ed; stroke: #fdba74; }
+      .payload { fill: #d97706; }
+      .loader { fill: #f1f5f9; stroke: #cbd5e1; }
+      .mapping { fill: #fffbeb; stroke: #f59e0b; }
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+      .gpu { fill: #f0fdf4; stroke: #86efac; }
+      .device { fill: #ffffff; stroke: #86efac; }
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+      .title { font: 700 25px system-ui, -apple-system, Segoe UI, sans-serif; fill: #0f172a; }
+      .subtitle { font: 500 13px system-ui, -apple-system, Segoe UI, sans-serif; fill: #64748b; }
+      .section { font: 700 15px system-ui, -apple-system, Segoe UI, sans-serif; fill: #1e293b; }
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+      .eyebrow { font: 700 10px system-ui, -apple-system, Segoe UI, sans-serif; letter-spacing: .8px; fill: #64748b; }
+      .box-title { font: 700 12px system-ui, -apple-system, Segoe UI, sans-serif; fill: #1e293b; }
+      .box-text { font: 500 10.5px system-ui, -apple-system, Segoe UI, sans-serif; fill: #475569; }
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+      .payload-text { font: 500 11px system-ui, -apple-system, Segoe UI, sans-serif; fill: #ffffff; }
+      .device-title { font: 700 9px system-ui, -apple-system, Segoe UI, sans-serif; fill: #166534; }
+      .slot-text { font: 700 8px system-ui, -apple-system, Segoe UI, sans-serif; fill: #ffffff; }
+      .slot-next-text { font: 700 8px system-ui, -apple-system, Segoe UI, sans-serif; fill: #166534; }
+      .arrow-label { font: 600 10.5px system-ui, -apple-system, Segoe UI, sans-serif; fill: #475569; }
+      .request-label { font: 600 10.5px system-ui, -apple-system, Segoe UI, sans-serif; fill: #6d28d9; }
+      .mapping-label { font: 600 10.5px system-ui, -apple-system, Segoe UI, sans-serif; fill: #b45309; }
+      .no-title { font: 700 12px system-ui, -apple-system, Segoe UI, sans-serif; fill: #b91c1c; }
+      .no-text { font: 500 10.5px system-ui, -apple-system, Segoe UI, sans-serif; fill: #7f1d1d; }
+      .request-path { fill: none; stroke: #7c3aed; stroke-width: 2; marker-end: url(#request-arrow); }
+      .mapping-path { fill: none; stroke: #d97706; stroke-width: 2; marker-end: url(#mapping-arrow); }
+      .runtime-path { fill: none; stroke: #2563eb; stroke-width: 2; marker-end: url(#runtime-arrow); }
+      .startup-path { fill: none; stroke: #94a3b8; stroke-width: 1.5; stroke-dasharray: 4 3; marker-end: url(#runtime-arrow); }
+    </style>
+  </defs>
+
+  <rect x="12" y="12" width="1256" height="826" rx="14" class="canvas"/>
+
+  <text id="title-text" x="40" y="50" class="title">No-AllGather DLO: TP-coordinate host sharing, independent engines</text>
+  <text x="40" y="74" class="subtitle">TP2 shown: each engine owns two devices; equivalent workers share one host payload per TP coordinate</text>
+
+  <!-- The control plane remains outside DLO. -->
+  <rect x="390" y="94" width="500" height="68" rx="10" class="router"/>
+  <text x="640" y="121" text-anchor="middle" class="section">External router / orchestrator</text>
+  <text x="640" y="144" text-anchor="middle" class="section-sub">independent request placement &#183; outside vLLM-Omni DLO</text>
+
+  <!-- One node contains independent engine runtimes over one shared physical backing. -->
+  <rect x="30" y="184" width="1220" height="634" rx="14" class="node"/>
+  <text x="52" y="211" class="eyebrow">ONE NODE &#183; PROCESS-LOCAL ENGINES OVER SHARED PHYSICAL HOST MEMORY</text>
+
+  <!-- Independent request routing from the external orchestrator. -->
+  <path d="M 500 162 C 456 181, 400 204, 332 232" class="request-path"/>
+  <text x="416" y="194" class="request-label">request A</text>
+  <path d="M 780 162 C 824 181, 880 204, 948 232" class="request-path"/>
+  <text x="864" y="194" class="request-label">request B</text>
+
+  <!-- Independent engine A. -->
+  <rect x="45" y="232" width="575" height="250" rx="12" class="engine"/>
+  <text x="68" y="261" class="section">Independent engine A</text>
+  <text x="68" y="281" class="section-sub">TP2 &#183; two rank-local workers &#183; own request schedule and DLO state</text>
+
+  <rect x="68" y="310" width="98" height="130" rx="8" class="loader"/>
+  <text x="117" y="335" text-anchor="middle" class="eyebrow">STARTUP &#215;2</text>
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+  <text x="117" y="377" text-anchor="middle" class="box-title">loaders</text>
+  <text x="117" y="402" text-anchor="middle" class="box-text">final transforms</text>
+  <text x="117" y="421" text-anchor="middle" class="box-text">publish / join</text>
+
+  <rect x="180" y="310" width="112" height="130" rx="8" class="mapping"/>
+  <text x="236" y="335" text-anchor="middle" class="eyebrow">HOST VIEWS &#215;2</text>
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+  <text x="236" y="377" text-anchor="middle" class="box-title">TP1 mmap</text>
+  <text x="236" y="402" text-anchor="middle" class="box-text">process-local</text>
+  <text x="236" y="421" text-anchor="middle" class="box-text">optional registration</text>
+
+  <rect x="306" y="310" width="120" height="130" rx="8" class="hook"/>
+  <text x="366" y="335" text-anchor="middle" class="eyebrow">RUNTIME &#215;2</text>
+  <text x="366" y="359" text-anchor="middle" class="box-title">DLO backends</text>
+  <text x="366" y="377" text-anchor="middle" class="box-title">+ block hooks</text>
+  <text x="366" y="402" text-anchor="middle" class="box-text">private streams</text>
+  <text x="366" y="421" text-anchor="middle" class="box-text">events + state</text>
+
+  <rect x="440" y="310" width="157" height="130" rx="8" class="gpu"/>
+  <text x="518" y="330" text-anchor="middle" class="eyebrow">TWO DEVICES</text>
+  <rect x="450" y="340" width="137" height="42" rx="6" class="device"/>
+  <text x="518" y="353" text-anchor="middle" class="device-title">TP0 &#183; GPU A0</text>
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+  <text x="486" y="370" text-anchor="middle" class="slot-text">SLOT 0 NOW</text>
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+  <text x="550" y="370" text-anchor="middle" class="slot-next-text">SLOT 1 NEXT</text>
+  <rect x="450" y="389" width="137" height="42" rx="6" class="device"/>
+  <text x="518" y="402" text-anchor="middle" class="device-title">TP1 &#183; GPU A1</text>
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+  <text x="486" y="419" text-anchor="middle" class="slot-text">SLOT 0 NOW</text>
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+  <text x="550" y="419" text-anchor="middle" class="slot-next-text">SLOT 1 NEXT</text>
+
+  <path d="M 166 375 L 180 375" class="startup-path"/>
+  <path d="M 292 375 L 306 375" class="runtime-path"/>
+  <path d="M 426 375 L 440 375" class="runtime-path"/>
+
+  <!-- Independent engine B. -->
+  <rect x="660" y="232" width="575" height="250" rx="12" class="engine"/>
+  <text x="683" y="261" class="section">Independent engine B</text>
+  <text x="683" y="281" class="section-sub">TP2 &#183; two rank-local workers &#183; own request schedule and DLO state</text>
+
+  <rect x="683" y="310" width="98" height="130" rx="8" class="loader"/>
+  <text x="732" y="335" text-anchor="middle" class="eyebrow">STARTUP &#215;2</text>
+  <text x="732" y="359" text-anchor="middle" class="box-title">rank-local</text>
+  <text x="732" y="377" text-anchor="middle" class="box-title">loaders</text>
+  <text x="732" y="402" text-anchor="middle" class="box-text">final transforms</text>
+  <text x="732" y="421" text-anchor="middle" class="box-text">publish / join</text>
+
+  <rect x="795" y="310" width="112" height="130" rx="8" class="mapping"/>
+  <text x="851" y="335" text-anchor="middle" class="eyebrow">HOST VIEWS &#215;2</text>
+  <text x="851" y="359" text-anchor="middle" class="box-title">TP0 mmap</text>
+  <text x="851" y="377" text-anchor="middle" class="box-title">TP1 mmap</text>
+  <text x="851" y="402" text-anchor="middle" class="box-text">process-local</text>
+  <text x="851" y="421" text-anchor="middle" class="box-text">optional registration</text>
+
+  <rect x="921" y="310" width="120" height="130" rx="8" class="hook"/>
+  <text x="981" y="335" text-anchor="middle" class="eyebrow">RUNTIME &#215;2</text>
+  <text x="981" y="359" text-anchor="middle" class="box-title">DLO backends</text>
+  <text x="981" y="377" text-anchor="middle" class="box-title">+ block hooks</text>
+  <text x="981" y="402" text-anchor="middle" class="box-text">private streams</text>
+  <text x="981" y="421" text-anchor="middle" class="box-text">events + state</text>
+
+  <rect x="1055" y="310" width="157" height="130" rx="8" class="gpu"/>
+  <text x="1133" y="330" text-anchor="middle" class="eyebrow">TWO DEVICES</text>
+  <rect x="1065" y="340" width="137" height="42" rx="6" class="device"/>
+  <text x="1133" y="353" text-anchor="middle" class="device-title">TP0 &#183; GPU B0</text>
+  <rect x="1072" y="359" width="58" height="16" rx="4" class="slot-current"/>
+  <text x="1101" y="370" text-anchor="middle" class="slot-text">SLOT 0 NOW</text>
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+  <text x="1165" y="370" text-anchor="middle" class="slot-next-text">SLOT 1 NEXT</text>
+  <rect x="1065" y="389" width="137" height="42" rx="6" class="device"/>
+  <text x="1133" y="402" text-anchor="middle" class="device-title">TP1 &#183; GPU B1</text>
+  <rect x="1072" y="408" width="58" height="16" rx="4" class="slot-current"/>
+  <text x="1101" y="419" text-anchor="middle" class="slot-text">SLOT 0 NOW</text>
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+  <text x="1165" y="419" text-anchor="middle" class="slot-next-text">SLOT 1 NEXT</text>
+
+  <path d="M 781 375 L 795 375" class="startup-path"/>
+  <path d="M 907 375 L 921 375" class="runtime-path"/>
+  <path d="M 1041 375 L 1055 375" class="runtime-path"/>
+
+  <!-- The runtimes are deliberately not coupled by a DLO collective. -->
+  <rect x="365" y="516" width="550" height="44" rx="8" class="no-collective"/>
+  <circle cx="395" cy="538" r="12" fill="#dc2626"/>
+  <path d="M 389 532 L 401 544 M 401 532 L 389 544" stroke="#ffffff" stroke-width="2.2"/>
+  <text x="420" y="534" class="no-title">No cross-engine DLO AllGather</text>
+  <text x="420" y="551" class="no-text">shared host pages do not couple request order, progress, or failure domains</text>
+  <text x="640" y="592" text-anchor="middle" class="section-sub">Ordinary TP / SP model collectives, when configured, remain inside each engine</text>
+
+  <!-- Each engine maps both TP-coordinate payloads from the shared host cache. -->
+  <path d="M 455 650 C 412 625, 340 610, 340 570 L 340 525 C 340 478, 278 454, 236 440" class="mapping-path"/>
+  <text x="286" y="617" text-anchor="middle" class="mapping-label">TP0 + TP1 mappings</text>
+  <path d="M 825 650 C 868 625, 940 610, 940 570 L 940 525 C 940 478, 893 454, 851 440" class="mapping-path"/>
+  <text x="994" y="617" text-anchor="middle" class="mapping-label">TP0 + TP1 mappings</text>
+
+  <!-- One node-local physical backing, visually beneath its consumers. -->
+  <rect x="58" y="650" width="242" height="140" rx="10" class="scope"/>
+  <text x="78" y="676" class="eyebrow">SHARING SCOPE</text>
+  <text x="78" y="702" class="box-title">TP2: two coordinate entries</text>
+  <text x="78" y="725" class="box-text">one physical copy per coordinate</text>
+  <text x="78" y="746" class="box-text">A/TP0 + B/TP0 &#8594; TP0 entry</text>
+  <text x="78" y="767" class="box-text">A/TP1 + B/TP1 &#8594; TP1 entry</text>
+
+  <rect x="330" y="650" width="620" height="140" rx="10" class="cache"/>
+  <text x="350" y="676" class="eyebrow">NODE-LOCAL IMMUTABLE HOST WEIGHT CACHE &#183; ONE COPY PER TP COORDINATE</text>
+  <rect x="370" y="692" width="260" height="60" rx="8" class="payload"/>
+  <text x="500" y="717" text-anchor="middle" class="payload-title">TP0 physical payload</text>
+  <text x="500" y="738" text-anchor="middle" class="payload-text">shared by A/TP0 + B/TP0</text>
+  <rect x="650" y="692" width="260" height="60" rx="8" class="payload"/>
+  <text x="780" y="717" text-anchor="middle" class="payload-title">TP1 physical payload</text>
+  <text x="780" y="738" text-anchor="middle" class="payload-text">shared by A/TP1 + B/TP1</text>
+  <text x="640" y="773" text-anchor="middle" class="box-text">Identity includes final layout + TP coordinate; engine and DP replica are excluded</text>
+
+  <rect x="980" y="650" width="242" height="140" rx="10" class="scope"/>
+  <text x="1000" y="676" class="eyebrow">WHAT STAYS PRIVATE</text>
+  <text x="1000" y="702" class="box-title">Per worker / process</text>
+  <text x="1000" y="725" class="box-text">mmap object and validation state</text>
+  <text x="1000" y="746" class="box-text">optional host registration</text>
+  <text x="1000" y="767" class="box-text">hooks, streams, events, GPU slots</text>
+</svg>
@@ -1,156 +1,127 @@
 # Distributed Layerwise Offloading

-Distributed layerwise offloading (DLO) extends block streaming to multi-device
-deployments. With AllGather enabled, each rank stores roughly `1 / dp_size` of
-the host weights and reconstructs each layer at runtime. Without AllGather,
-each rank streams a complete block independently. Compatible TP1 deployments
-can share checkpoint-backed host pages among processes on the same node;
-otherwise DLO streams the ordinary loader's rank-local tensors.
+Distributed layerwise offloading (DLO) streams DiT blocks through two device
+buffers instead of keeping the complete DiT in HBM. It supports sharded host
+weights with AllGather or independently streamed rank-local weights. The
+no-AllGather path can optionally share node-local host weights through mmap.

-See the [DLO feature design](../../../design/feature/offloader/distributed_layerwise_offload.md)
-for the implementation contract and compatibility matrix.
+See the [shared DLO component architecture](../../../design/feature/offloader/distributed_layerwise_offload.md#architecture)
+for ownership and lifecycle coordination. The mode-specific internals are
+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).

-## Execution model
+## Choose a mode

-DLO overlaps three operations with a fixed two-block device buffer:
+| Mode | Use when | Host weights | Runtime synchronization |
+| --- | --- | --- | --- |
+| 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 |

-```text
-Compute stream:  [Layer N]          [Layer N+1]        [Layer N+2]
-H2D stream:      [H2D shard N+1]    [H2D shard N+2]
-AllGather:       [AG N+1]           [AG N+2]
-Slots:           slot 0: Layer N    slot 1: Layer N+1
-```
-
-AllGather communicates only request-independent weight shards, so data-
-parallel ranks may process different requests concurrently.
+AllGather is normally the best choice for synchronized DP. Host weight cache mode
+targets independently scheduled replicas, especially repeated TP engines on
+one node.

 ## Usage

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

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

-# Sequence parallel deployment
+# Share final host weights; prefer registered direct H2D under the pinned-memory policy
 vllm serve /path/to/model --omni \
  --enable-distributed-layerwise-offload \
-  --usp 4
+  --dlo-no-use-allgather \
+  --dlo-use-host-weight-cache
+
+# Apply an 80 GiB per-worker safety ceiling to the same registration attempt
+vllm serve /path/to/model --omni \
+  --enable-distributed-layerwise-offload \
+  --tensor-parallel-size 2 \
+  --dlo-no-use-allgather \
+  --dlo-use-host-weight-cache \
+  --dlo-host-weight-cache-pin-limit-gib 80
 ```

-```python
-from vllm_omni import Omni
-
-omni = Omni(
-    model="/path/to/model",
-    enable_distributed_layerwise_offload=True,
-    dlo_use_allgather=True,
-)
-```
-
-## Flags
+## Relevant options

 | Flag | Meaning | Default |
 | --- | --- | --- |
 | `--enable-distributed-layerwise-offload` | Enable DLO | `false` |
-| `--data-parallel-size N` | DP ranks and AllGather weight-sharding group | `1` |
-| `--dlo-use-allgather` | Shard host weights and reconstruct with AllGather | `true` |
-| `--dlo-no-use-allgather` | Stream complete rank-local blocks without a DLO weight collective | `false` |
-| `--dlo-resident-layers N` | Keep N leading main-DiT blocks on device; requires no-AllGather and model-declared resident paths | `0` |
+| `--dlo-no-use-allgather` | Stream complete rank-local blocks independently | `false` |
+| `--dlo-use-host-weight-cache` | Use the final-layout cache and prefer registration when pinned memory is enabled | `false` |
+| `--dlo-host-weight-cache-pin-limit-gib GIB` | Optional per-worker registration ceiling; zero means no additional ceiling | `0` |
+| `--dlo-resident-layers N` | Keep eligible leading DiT blocks resident in HBM | `0` |

-## Host-weight loading
+No-AllGather DLO automatically uses a compatible checkpoint mmap plan. When
+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.

-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.
+## Operational notes

-The shared-mmap optimization in this phase is supported only with TP1. TP
-greater than one falls back before model mutation to ordinary TP-aware loading.
-DLO may still consume those TP-local tensors, but this is a compatibility path:
-it does not share checkpoint-backed runtime weights across DP replicas and
-provides no shared-mmap host-memory guarantee.
+- All workers that should share must see the same local, disk-backed cache
+  directory. Do not use tmpfs or a cross-node filesystem for this version.
+- Cache entries are immutable and validated before use. A cache or registration
+  failure keeps the ordinary loader weights or falls back to bounded staging.
+- The bounded-staging fallback is a memory-first mode: recurrent pageable-mmap to
+  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.

-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.
+## Scheduling constraints

-With direct checkpoint mmap, the loader:
+AllGather ranks must request blocks in the same collective order. Concurrent DP
+requests may use different prompts, but they must follow the same denoising and
+block-execution path and set the same explicit `num_inference_steps`.

-1. saves non-persistent buffers such as RoPE frequencies;
-2. moves the normally created transformer to the meta device;
-3. loads checkpoint tensors as mmap views backed by the shared OS page cache;
-4. applies any loader-owned bounded layout adapters while packing blocks;
-5. restores saved non-persistent buffers; and
-6. preserves `post_load_weights()` and `validate_loaded_weights()` lifecycle
-   hooks.
-
-For AllGather with a group larger than one, each process copies only its
-persistent shard and then releases the source mapping. For no-AllGather, each
-process keeps the mapping open and packs complete blocks through two bounded
-pinned staging slots. Processes mapping the same files on one node share the
-immutable pages; no-AllGather still performs a complete-block H2D copy in each
-process.
-
-When the effective DLO group size is one, `dlo_use_allgather=True` does not
-perform a collective and uses the same rank-local transfer behavior.
-
-## Declarative topology
-
-Models may declare an `OffloadPlan` instead of embedding offload logic:
-
-```python
-from vllm_omni.diffusion.offloader import OffloadPlan
-
-
-class MyPipeline(nn.Module):
-    _dit_modules = ["transformer"]
-    _offload_plan = OffloadPlan(
-        block_attrs={"transformer": ("blocks",)},
-        offload_submodules={"context_encoder": "layers"},
-    )
-```
-
-When no plan exists, discovery falls back to
-`_layerwise_offload_blocks_attrs` and then heuristic attribute lookup.
-
-## Data-parallel concurrency
-
-With `data_parallel_size > 1` and AllGather enabled, the scheduler can process
-up to `dp_size` requests per denoising step. Every concurrent request must set
-the same explicit `num_inference_steps`; `None` is rejected because every rank
-must enter each collective.
+No-AllGather workers do not have this DLO lockstep requirement. Ordinary TP or
+SP model collectives still synchronize ranks within each engine.

 ## Limitations

- Direct checkpoint mmap currently requires TP1. TP greater than one is
-  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.
+- Direct checkpoint mmap currently requires TP1. The host weight cache can share
+  final ordinary-loader layouts between matching TP coordinates at TP1 or TP>1.
 - Per-tensor online FP8 linears use the ordinary loader and can run with either
-  DLO transfer path. With AllGather, every rank temporarily materializes the
-  complete FP8 model in host memory before DLO retains only its shard. Other
-  online quantization methods require no-AllGather until their runtime layouts
-  are validated.
- Resident leading layers require `--dlo-no-use-allgather` and a model
-  `OffloadPlan` that declares eligible `resident_dit_paths`.
- DP concurrency requires an explicit, identical inference-step count.
-
-Sharing transformed TP or quantized runtime layouts through a normalized mmap
-cache is a follow-up design in
-[RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195), not part of
-the direct-checkpoint path.
+  DLO transfer path. Other online quantization methods require no-AllGather
+  until their runtime layouts are validated.
+- Host weight cache v1 rejects quantized, non-contiguous, aliased/tied,
+  device-only, HSDP, expert-parallel, CFG-parallel, and PP layouts.
+- HSDP plus DLO AllGather is unsupported, and host weight cache v1 rejects HSDP.
+- The host weight cache changes steady-state host backing, not startup loading:
+  each worker still performs ordinary loading and full content validation.
+- Skip-load-on-hit, cache eviction, and cross-node sharing remain follow-up
+  work in
+  [RFC #6195](https://github.com/vllm-project/vllm-omni/issues/6195).

 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.
@@ -908,6 +908,52 @@ 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]
@@ -12,6 +12,7 @@ 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
@@ -309,6 +310,7 @@ 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

@@ -346,7 +348,7 @@ def test_dlo_plan_loads_component_sources_outside_planned_dit(monkeypatch):
        backing_kind="checkpoint_mmap",
        bindings={
            "transformer.weight": TensorBinding(
-                checkpoint_key="weight",
+                storage_key="weight",
                file_path="unused",
            )
        },
@@ -374,8 +376,9 @@ def test_dlo_plan_loads_component_sources_outside_planned_dit(monkeypatch):
    assert loader.take_host_weight_plan() is plan


-def test_dlo_plan_fallback_runs_ordinary_loader(monkeypatch):
+def test_dlo_plan_fallback_retains_ordinary_loader_weights_by_default(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,
@@ -400,11 +403,202 @@ def test_dlo_plan_fallback_runs_ordinary_loader(monkeypatch):
        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,
@@ -0,0 +1,260 @@
+# 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
@@ -0,0 +1,346 @@
+# 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]
@@ -18,6 +18,7 @@ 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,
@@ -546,6 +547,31 @@ 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):
@@ -636,6 +662,24 @@ 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
    ):
@@ -765,6 +809,140 @@ 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()}
@@ -788,7 +966,7 @@ class TestMmapWeightLoading:
            backing_kind="checkpoint_mmap",
            bindings={
                name: TensorBinding(
-                    checkpoint_key=name,
+                    storage_key=name,
                    file_path=str(weight_file),
                )
                for name in weights
@@ -859,6 +1037,292 @@ 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):
@@ -1164,7 +1628,7 @@ class TestMmapValidation:
        assert result.fallback_reason is None
        assert result.plan is not None
        assert result.plan.bindings["transformer.blocks.0.weight"] == TensorBinding(
-            checkpoint_key="blocks.0.weight",
+            storage_key="blocks.0.weight",
            file_path=str(checkpoint_file),
        )
        assert result.plan.planned_source_prefixes == frozenset({"transformer."})
@@ -1454,7 +1918,7 @@ class TestMmapValidation:

        assert result.plan is not None
        assert result.plan.bindings["transformer.proj_in.weight"] == TensorBinding(
-            checkpoint_key="proj_in.weight",
+            storage_key="proj_in.weight",
            file_path=str(checkpoint_file),
        )

@@ -12,11 +12,13 @@ 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,
@@ -204,6 +206,29 @@ 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
@@ -328,6 +328,9 @@ 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",
        ]
@@ -339,9 +342,15 @@ 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 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


@@ -11,6 +11,7 @@ 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
@@ -687,6 +688,10 @@ 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)
    pin_cpu_memory: bool = True
    diffusion_compile_granularity: Literal["regional", "full"] = "regional"
@@ -749,6 +754,20 @@ 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):
@@ -382,6 +382,10 @@ 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
    # Diffusion-specific debug and observability knobs.
    enable_diffusion_pipeline_profiler: bool | None = None
@@ -708,6 +708,16 @@ 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

@@ -936,6 +946,18 @@ 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', "
@@ -14,6 +14,7 @@ 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,
@@ -440,10 +441,13 @@ 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:
+                if _dist_offload and not _select_host_weight_cache:
                    modules = ModuleDiscovery.discover(model)
                    plan_result = build_checkpoint_mmap_plan(
                        model,
@@ -455,6 +459,16 @@ 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

@@ -496,7 +510,8 @@ class DiffusersPipelineLoader:
                        )
                    if _dist_offload and plan_result is not None:
                        logger.info(
-                            "DLO direct checkpoint mmap unavailable; using ordinary loader: %s",
+                            "DLO direct checkpoint mmap unavailable [%s]; using ordinary loader: %s",
+                            plan_result.fallback_code or "unknown",
                            plan_result.fallback_reason,
                        )
                    logger.debug("Loading weights on %s ...", load_device)
@@ -521,7 +536,110 @@ class DiffusersPipelineLoader:
                logger.info("Quantization complete, offloaded model back to CPU")

        self._apply_skip_softmax_calibration(model)
-        return model.eval()
+        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
+
+                tp_rank = int(get_tensor_model_parallel_rank())
+            except Exception as exc:
+                logger.warning(
+                    "DLO host weight cache unavailable [parallel_rank_unavailable]: "
+                    "cannot resolve TP rank for TP=%d: %s",
+                    tp_size,
+                    exc,
+                )
+                return
+
+        modules = ModuleDiscovery.discover(model)
+        from vllm_omni.diffusion.model_loader.host_weight_cache import (
+            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:
@@ -0,0 +1,574 @@
+# 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",
+]
@@ -0,0 +1,287 @@
+# 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",
+]
@@ -9,6 +9,7 @@ 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
@@ -28,7 +29,7 @@ TensorTransform = Callable[[torch.Tensor], torch.Tensor]
 class TensorBinding:
    """One runtime tensor backed by one safetensors entry."""

-    checkpoint_key: str
+    storage_key: str
    file_path: str
    transform: TensorTransform | None = None

@@ -37,9 +38,16 @@ class TensorBinding:
 class HostWeightPlan:
    """Complete, prevalidated host backing consumed by an offload backend."""

-    backing_kind: str
+    backing_kind: Literal["checkpoint_mmap", "host_weight_cache"]
    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)
@@ -48,6 +56,7 @@ class HostWeightPlanResult:

    plan: HostWeightPlan | None
    fallback_reason: str | None = None
+    fallback_code: str | None = None


 class _PlanIncompatibleError(RuntimeError):
@@ -258,11 +267,11 @@ 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.checkpoint_key not in available:
+                if binding.storage_key not in available:
                    raise _PlanIncompatibleError(
-                        f"checkpoint key {binding.checkpoint_key!r} for {runtime_name!r} is missing from {file_path}"
+                        f"checkpoint key {binding.storage_key!r} for {runtime_name!r} is missing from {file_path}"
                    )
-                source = handle.get_slice(binding.checkpoint_key)
+                source = handle.get_slice(binding.storage_key)
                source_shape = tuple(source.get_shape())
                source_dtype = _SAFETENSORS_DTYPES.get(source.get_dtype())
                if source_shape != tuple(target.shape):
@@ -287,11 +296,19 @@ def build_checkpoint_mmap_plan(
 ) -> HostWeightPlanResult:
    """Build a complete direct-checkpoint plan or return a fallback reason."""
    if tensor_parallel_size != 1:
-        return HostWeightPlanResult(None, f"TP={tensor_parallel_size} requires the ordinary loader")
+        return HostWeightPlanResult(
+            None,
+            f"TP={tensor_parallel_size} requires the ordinary loader",
+            "unsupported_tp",
+        )
    if use_hsdp:
-        return HostWeightPlanResult(None, "HSDP requires the ordinary loader")
+        return HostWeightPlanResult(None, "HSDP requires the ordinary loader", "unsupported_hsdp")
    if online_quantization:
-        return HostWeightPlanResult(None, "online quantization requires the ordinary loader")
+        return HostWeightPlanResult(
+            None,
+            "online quantization requires the ordinary loader",
+            "unsupported_online_quantization",
+        )

    remap_fn = getattr(type(pipeline), "_remap_ckpt_key", None)
    if not callable(remap_fn):
@@ -328,14 +345,14 @@ def build_checkpoint_mmap_plan(
                )
            checkpoint_key, file_path = model_to_ckpt[runtime_name]
            bindings[runtime_name] = TensorBinding(
-                checkpoint_key=checkpoint_key,
+                storage_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))
+        return HostWeightPlanResult(None, str(exc), "checkpoint_plan_incompatible")

    return HostWeightPlanResult(
        HostWeightPlan(
@@ -71,8 +71,9 @@ 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, if ordinary
-            weight materialization was skipped.
+        host_weight_plan: Exact loader-produced backing plan. Checkpoint plans
+            may have skipped ordinary materialization; host weight cache plans
+            describe already-valid ordinary tensors that can be remapped.

    Returns:
        OffloadBackend instance or None if offloading disabled
@@ -85,10 +86,14 @@ 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(
                "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:
@@ -96,7 +101,7 @@ 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 host_weight_plan is not None:
+        if plan_requires_backend:
            raise RuntimeError(
                "The loader skipped ordinary weight materialization for DLO, "
                "but this platform cannot create the required offload backend"
@@ -112,7 +117,7 @@ def get_offload_backend(
        try:
            device = current_omni_platform.get_torch_device()
        except (NotImplementedError, AttributeError) as exc:
-            if host_weight_plan is not None:
+            if plan_requires_backend:
                raise RuntimeError(
                    "The loader skipped ordinary weight materialization for DLO, "
                    "but the target device could not be resolved"
@@ -31,6 +31,9 @@ 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":
@@ -101,6 +104,7 @@ 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:
@@ -136,6 +140,7 @@ 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,
        )


@@ -0,0 +1,280 @@
+# 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
@@ -18,7 +18,9 @@ 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
@@ -27,6 +29,7 @@ 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,
 )
@@ -34,6 +37,12 @@ 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,
@@ -93,6 +102,7 @@ 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()
@@ -445,17 +455,11 @@ class DistributedLayerwiseOffloadHook(ModelHook):
    # ------------------------------------------------------------------ #

    @staticmethod
-    def _pack_mmap_sources(
-        cpu_sources: dict[torch.dtype, list[dict[str, Any]]],
-        metadata: dict[torch.dtype, list[dict[str, Any]]],
-        slot_buffers: dict[torch.dtype, torch.Tensor],
-    ) -> dict[torch.dtype, torch.Tensor]:
-        staged: dict[torch.dtype, torch.Tensor] = {}
-        for dtype, metas in metadata.items():
-            total_numel = sum(meta["numel"] for meta in metas)
-            destination = slot_buffers[dtype][:total_numel]
-            sources = cpu_sources[dtype]
-            for source_info, meta in zip(sources, metas, strict=True):
+    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):
@@ -468,6 +472,21 @@ class DistributedLayerwiseOffloadHook(ModelHook):
                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]]],
+        metadata: dict[torch.dtype, list[dict[str, Any]]],
+        slot_buffers: dict[torch.dtype, torch.Tensor],
+    ) -> dict[torch.dtype, torch.Tensor]:
+        staged: dict[torch.dtype, torch.Tensor] = {}
+        for dtype, metas in metadata.items():
+            total_numel = sum(meta["numel"] for meta in metas)
+            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)
                start = meta["offset"]
                physical_storage = destination[start : start + meta["numel"]]
                if source.is_contiguous():
@@ -481,6 +500,32 @@ 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]
@@ -514,6 +559,16 @@ 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:
+                with current_omni_platform.stream(self.copy_stream):
+                    self._copy_mmap_sources_to_device(
+                        self.cpu_sources,
+                        self.metadata,
+                        gpu_weights,
+                        non_blocking=non_blocking,
+                    )
+                    evt.record(self.copy_stream)
+            else:
                cpu_weights = self._stage_mmap_sources(slot) if self.rank_local_mmap else self.cpu_shards
                with current_omni_platform.stream(self.copy_stream):
                    for dtype, cpu_shard in cpu_weights.items():
@@ -734,6 +789,7 @@ 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]] = []
@@ -804,6 +860,14 @@ 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]
@@ -901,8 +965,11 @@ 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."""
@@ -1023,7 +1090,7 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
                    framework="pt",
                    device="cpu",
                )
-            tensor = file_cache[binding.file_path].get_tensor(binding.checkpoint_key)
+            tensor = file_cache[binding.file_path].get_tensor(binding.storage_key)
            if is_parameter:
                replacement = torch.nn.Parameter(tensor, requires_grad=target.requires_grad)
                parent._parameters[leaf_name] = replacement
@@ -1103,6 +1170,179 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
                f"(first 5: {remaining_meta[:5]})."
            )

+    def _load_weights_from_host_weight_cache(
+        self,
+        pipeline: nn.Module,
+        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()):
+            raise RuntimeError("Host weight cache bindings cannot carry deferred checkpoint transforms")
+
+        cache_files = {binding.file_path for binding in plan.bindings.values()}
+        if plan.expected_file_digests is None or set(plan.expected_file_digests) != cache_files:
+            raise RuntimeError("Host weight cache plan does not cover every shard digest")
+        for file_path in sorted(cache_files):
+            try:
+                actual_digest = file_digest(Path(file_path))
+            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()
+        for dit_name, dit_module in zip(modules.dit_names, modules.dits):
+            required_names.update(f"{dit_name}.{name}" for name, _ in dit_module.named_parameters())
+            for name, _ in dit_module.named_buffers():
+                parent_path, _, leaf_name = name.rpartition(".")
+                owner = dit_module.get_submodule(parent_path)
+                if leaf_name not in owner._non_persistent_buffers_set:
+                    required_names.add(f"{dit_name}.{name}")
+        if required_names != set(plan.bindings):
+            missing = sorted(required_names - set(plan.bindings))
+            unexpected = sorted(set(plan.bindings) - required_names)
+            raise RuntimeError(
+                "Host weight cache plan coverage changed after loader validation: "
+                f"missing={missing[:5]}, unexpected={unexpected[:5]}"
+            )
+
+        file_cache: dict[str, Any] = {}
+        replacements: list[tuple[torch.Tensor, torch.Tensor]] = []
+        mapped_sources: dict[str, list[torch.Tensor]] = {}
+        try:
+            # Validate and map every tensor before mutating the pipeline. A
+            # missing/corrupt shard therefore leaves the ordinary tensors intact.
+            for runtime_name, binding in plan.bindings.items():
+                parent_path, _, leaf_name = runtime_name.rpartition(".")
+                try:
+                    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.

@@ -1443,17 +1683,41 @@ 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":
-                raise ValueError(f"Unsupported DLO host-weight backing: {self.host_weight_plan.backing_kind}")
+            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}")
            if self._using_rank_local_mmap:
                logger.info(
-                    "DLO rank-local mmap storage enabled: checkpoint pages are "
-                    "node-shared; each worker owns only two bounded host staging slots"
+                    "DLO rank-local mmap storage enabled: file-backed pages are node-shared; "
+                    "transfer setup will select registered direct H2D or bounded host staging"
                )
        else:
            remaining_meta = [
@@ -1623,6 +1887,9 @@ 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:
@@ -1632,17 +1899,14 @@ 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:
+        if self._using_rank_local_mmap and not self._using_registered_mmap:
            unified_cpu_staging = self._allocate_shared_cpu_staging_buffers(
                all_hooks,
                self._resident_layer_group,
@@ -1724,6 +1988,7 @@ 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
@@ -1746,8 +2011,11 @@ class DistributedLayerwiseOffloadBackend(OffloadBackend):
        if not self.enabled and not hasattr(self, "_mmap_file_cache"):
            return

-        if self._using_rank_local_mmap:
+        # Copy and communication streams are asynchronous in both AllGather
+        # and no-AllGather modes. Drain all device work before hooks, process
+        # 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:
@@ -1763,9 +2031,11 @@ 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._using_mmap = False
        self._using_rank_local_mmap = False
+        self._using_registered_mmap = False
        self.enabled = False
        logger.info("Distributed layer-wise offloading disabled")

@@ -0,0 +1,62 @@
+# 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",
+]
@@ -859,10 +859,17 @@ class DiffusionWorker:

    def shutdown(self) -> None:
        """Shutdown the worker and cleanup distributed environment."""
+        try:
            if self.model_runner is not None:
                mgr = getattr(self.model_runner, "kv_transfer_manager", None)
+                try:
+                    offload_backend = getattr(self.model_runner, "offload_backend", None)
+                    if offload_backend is not None:
+                        offload_backend.disable()
+                finally:
                    if mgr is not None:
                        mgr.shutdown_prefetch()
+        finally:
            destroy_distributed_env()


@@ -545,6 +545,10 @@ 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
    boundary_ratio: float | None = None
    flow_shift: float | None = None
@@ -1040,6 +1040,10 @@ 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),
            "enforce_eager": False if kwargs.get("enforce_eager") is None else kwargs.get("enforce_eager"),
            "diffusion_compile_granularity": (
@@ -722,6 +722,25 @@ 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,
Author	SHA1	Message	Date
Hongsheng Liu	58eebee14b	[Diffusion] Simplify DLO host plan setup Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 23:00:31 +08:00
Hongsheng Liu	6bcd350301	[Diffusion] Prefer host-cache registration when pinned Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 18:13:44 +08:00
Hongsheng Liu	859003fb97	[Diffusion] Gate CUDA read-only host registration Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:36 +08:00
Hongsheng Liu	42c6ae9007	[Diffusion] Document TP host layout identity Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:36 +08:00
Hongsheng Liu	16642dc2e8	[Diffusion] Make DLO host weight cache opt-in Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:36 +08:00
Hongsheng Liu	75af33a76f	[Diffusion] Address DLO host cache review feedback Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:36 +08:00
Hongsheng Liu	44d3245a6d	[Diffusion] Clear handled CUDA registration errors Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:36 +08:00
Hongsheng Liu	010b3c1d74	[Diffusion] Make no-AllGather host weight cache automatic Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:36 +08:00
Hongsheng Liu	3bb7547a0f	[Diffusion] Rename runtime cache to host weight cache Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:36 +08:00
Hongsheng Liu	ee665fcfc3	[Docs] Add AllGather DLO architecture diagram Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:16 +08:00
Hongsheng Liu	649558e1a1	[Docs] Show TP2 devices in DLO architecture Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:16 +08:00
Hongsheng Liu	3b9727c1e1	[Docs] Reorder no-AllGather architecture diagram Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:16 +08:00
Hongsheng Liu	47153d86e6	[Docs] Visualize no-AllGather shared host weights Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:16 +08:00
Hongsheng Liu	1ccdc2e80e	[Docs] Separate DLO transfer mode design Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:23:16 +08:00
Hongsheng Liu	8f93a74951	[Docs] Remove stale DLO validation results Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:22:52 +08:00
Hongsheng Liu	4acb3b1387	[Docs] Add DLO component architecture Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:21:59 +08:00
Hongsheng Liu	6b7b5368f2	[Diffusion] Clarify runtime cache module boundary Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:21:59 +08:00
Hongsheng Liu	6751c1f404	[Diffusion] Address runtime cache review feedback Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:21:59 +08:00
Hongsheng Liu	c1196216a3	[Diffusion] Stabilize runtime cache identities Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:21:28 +08:00
Hongsheng Liu	23ab14a15b	[Diffusion] Register runtime cache for direct DLO H2D Avoid recurrent mmap-to-pinned packing in no-AllGather DLO by registering transform-complete runtime-cache mappings under an explicit per-worker budget. Copy mapped tensors directly into the rotating device buffers while preserving bounded-staging fallback and ordered shutdown cleanup. Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:21:28 +08:00
Hongsheng Liu	67f15e73cd	[Diffusion] Document runtime cache hardware tradeoffs Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:21:28 +08:00
Hongsheng Liu	59549968cf	[Diffusion] Tighten runtime cache compatibility guards Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:21:28 +08:00
Hongsheng Liu	d30ccdc315	[Diffusion] Add node-local runtime weight cache for DLO Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-19 11:21:01 +08:00