[diffusion][feature] Add component-selective offload policies (#5929)
Signed-off-by: lishunyang12 <lishunyang12@163.com>
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@@ -5,23 +5,27 @@ The three strategies have separate user and design pages:
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| Strategy | User guide | Design contract |
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| --- | --- | --- |
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| Model-level | [Guide](../../../user_guide/diffusion/offloader/cpu_offload.md) | [Design](cpu_offload.md) |
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| Model-level | [Guide](../../../user_guide/diffusion/offloader/module_offload.md) | [Design](module_offload.md) |
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| Layerwise | [Guide](../../../user_guide/diffusion/offloader/layerwise_offload.md) | [Design](layerwise_offload.md) |
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| Distributed layerwise | [Guide](../../../user_guide/diffusion/offloader/distributed_layerwise_offload.md) | [Design](distributed_layerwise_offload.md) |
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## Strategy selection
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`OffloadConfig.from_od_config()` converts diffusion configuration into one
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`OffloadStrategy`. If callers set multiple flags, selection is deterministic:
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`OffloadStrategy`. The public config separates component selection from
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policy:
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1. `DISTRIBUTED_LAYERWISE`;
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2. `LAYERWISE`;
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3. `MODEL_LEVEL`; and
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4. `NONE`.
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- `mode="module"` selects model-level offload;
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- `mode="layer"` with rank-local transfers selects ordinary layerwise
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offload; and
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- AllGather transfer or resident layers selects the distributed layerwise
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backend that implements those capabilities.
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The factory logs when a higher-priority strategy disables another. It derives
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parallel and HSDP state from `DiffusionParallelConfig`; callers do not provide
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a separate offload group size.
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The compatibility boolean flags retain their historical priority (distributed
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layerwise, layerwise, then model-level). A compact config rejects a conflicting
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legacy strategy or non-default legacy DLO tuning. The factory derives parallel
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and HSDP state from `DiffusionParallelConfig`; callers do not provide a
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separate offload group size.
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`get_offload_backend()` then validates platform offload support, resolves the
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device, and creates exactly one backend. Returning `None` means offloading is
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@@ -39,6 +43,11 @@ Every backend implements `OffloadBackend`:
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`disable()` does not promise to restore the pipeline's original device
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placement. The caller owns any subsequent rematerialization.
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An enable failure must remove every partially installed hook. Rank-local
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backends restore ordinary tensors so the pipeline can be retried. A failed
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multi-rank AllGather startup must not enter unmatched recovery collectives;
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that worker is safely discarded after local resource cleanup.
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Hooks are registered through `HookRegistry` and `ModelHook`; offload backends
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must use distinct hook names and remove only hooks they own.
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@@ -55,13 +64,13 @@ Dotted paths are supported. Legacy pipelines may use the fallback scan of
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well-known attribute names, but new integrations should declare components
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explicitly.
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Ordinary layerwise offload obtains topology from declared
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`_layerwise_offload_blocks_attrs` (with a deprecated singular-name
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compatibility path). Distributed layerwise offload may instead use a pipeline
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`OffloadPlan`, then falls back to declared block attributes and its documented
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compatibility discovery. Discovery metadata describes structure only; the
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backend remains responsible for transfer, synchronization, and storage
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ownership.
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Both layerwise backends first consume pipeline `OffloadPlan` metadata. A
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plan's `block_attrs` maps each DiT path to its ordered block containers, while
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`encoder_block_attrs` declares streamable encoder stacks. DiTs absent from the
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plan fall back to `_layerwise_offload_blocks_attrs` (including the deprecated
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singular-name compatibility path). Discovery metadata describes structure
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only; the backend remains responsible for transfer, synchronization, and
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storage ownership.
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## Cross-strategy invariants
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@@ -1,10 +1,11 @@
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# Distributed Layerwise Offload
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This document describes distributed layerwise offload (DLO) for diffusion
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models. DLO keeps only a small number of DiT blocks on the accelerator and
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streams the remaining blocks from host memory. The distributed backend can
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either shard those host-side weights across an existing parallel group or keep
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complete rank-local block sources and avoid an additional collective.
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models. DLO keeps only a small number of selected DiT or text-encoder blocks
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on the accelerator and streams the remaining blocks from host memory. The
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distributed backend can either shard those host-side weights across an
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existing parallel group or keep complete rank-local block sources and avoid
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an additional collective.
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For user-facing commands, see the
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[distributed layerwise offloading guide](../../../user_guide/diffusion/offloader/distributed_layerwise_offload.md)
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@@ -38,10 +39,11 @@ detailed contracts and validation boundaries.
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## Status
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DLO is implemented for multi-device diffusion execution. The default
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AllGather path is the primary path for DP and SP deployments. The
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`--dlo-no-use-allgather` path streams complete blocks independently and adds no
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DLO weight collective.
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DLO is implemented for multi-device diffusion execution. The default public
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transfer is `rank-local`; setting a selected component's
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`layer_options.NAME.weight_transfer` to `allgather` enables weight sharding across a
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compatible DP or SP group. Transfers are configured independently for `dit`
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and `text_encoder`.
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Host storage is selected separately from the transfer protocol. The loader can
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produce a direct-checkpoint mmap plan for a proven-compatible runtime layout;
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@@ -109,7 +111,7 @@ checkpoint adapter beside the ordinary loader.
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### AllGather path
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With the default `dlo_use_allgather=True`, each rank stores approximately
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For a component configured with `weight_transfer="allgather"`, each rank stores approximately
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`1 / group_size` of each streamable block in pinned host memory. The next
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block's shard is copied to a device buffer and reconstructed with
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`all_gather_into_tensor` on a communication stream while the current block is
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@@ -132,9 +134,8 @@ source used to prepare each rank's persistent shard. They can be closed after
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shard preparation. Across the AllGather group, those private shards total
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approximately one runtime model copy.
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An effective DLO group size of one performs no collective, even when
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`dlo_use_allgather=True`; it follows the rank-local transfer path described
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below.
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An effective DLO group size of one performs no collective; it follows the
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rank-local transfer path described below.
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When DP is greater than one, the engine can process one request per DP rank in
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the same denoising wave. Because AllGather is a collective, all participating
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@@ -142,9 +143,10 @@ requests must take the same execution path at every denoising step.
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### Rank-local path without DLO AllGather
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With `--dlo-no-use-allgather`, DLO forces its internal offload shard size to
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one and streams complete blocks using H2D copies only. The host backing may be
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either a loader-approved checkpoint mapping or ordinary runtime tensors.
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With `weight_transfer="rank-local"`, DLO forces the affected component's internal
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offload shard size to one and streams complete blocks using H2D copies only.
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The host backing may be either a loader-approved checkpoint mapping or
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ordinary runtime tensors.
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For direct mmap, each process retains immutable safetensors views and uses two
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bounded pinned host staging slots. Processes on the same node that map the same
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@@ -204,7 +206,7 @@ adopt it.
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> optimizations without changing AllGather behavior.
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Final-layout Host Weight Runtime (HWR) backing is opt-in and currently applies
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only to no-AllGather DLO. Enable it with
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only to a selected rank-local DiT. Enable it with
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`--host-weight-runtime-mode preferred` (or `required`) and
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`--host-weight-runtime-root <node-local-root>`.
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@@ -422,8 +424,8 @@ failure.
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collective. When the model declares the final-layout contract, HWR keys the
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reusable artifact by TP rank/size and SP layout.
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- **HSDP + SP:** the general parallel configuration permits HSDP over SP, but
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DLO must use `--dlo-no-use-allgather`. HSDP remains responsible for weight
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materialization and synchronization.
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each selected component must use `weight_transfer="rank-local"`. HSDP remains
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responsible for weight materialization and synchronization.
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- **HSDP + DP or TP:** rejected independently by the diffusion parallel
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configuration.
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@@ -447,8 +449,9 @@ recorded layouts (they all keep plain 1-byte dtypes over ordinary strided
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views: online INT8 has an int8 weight plus fp32 scale, online MXFP8 has an
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fp8 weight plus e8m0 block scale, each with a contiguous or transposed-view
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finalized tensor, all covered by the same physical-order packing as online
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FP8). Other online methods must use `--dlo-no-use-allgather` or disable
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online quantization until their runtime layouts are validated.
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FP8). Other online methods must set
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`layer_options.dit.weight_transfer="rank-local"` or disable online quantization
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until their runtime layouts are validated.
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The Host Weight Runtime representation, publication, and no-AllGather consumer
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contracts are merged; see
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@@ -36,12 +36,14 @@ dependencies, not incidental global synchronization, define correctness.
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## Topology contract
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A DiT declares ordered block-container attributes using
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`_layerwise_offload_blocks_attrs` or an `OffloadPlan`. Each resolved item must
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be an executable `nn.Module`; ordering must match forward execution. Multiple
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containers are concatenated in execution order.
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`_layerwise_offload_blocks_attrs` or an `OffloadPlan`. A text encoder declares
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its block-list paths through `OffloadPlan.encoder_block_attrs`. Each resolved
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item must be an executable `nn.Module`; ordering must match forward execution.
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Multiple containers are streamed as separate hook rings.
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Non-block DiT modules, encoders, VAEs, and declared resident modules are not
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streamed by this backend.
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Non-block state of a selected DiT or text encoder remains device resident.
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Unselected components, VAEs, and declared resident modules are not streamed by
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this backend.
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## Invariants and limitations
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@@ -50,7 +52,8 @@ streamed by this backend.
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- Blocks with heterogeneous shapes and dtypes use their recorded metadata;
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implementations must not assume identical blocks.
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- The ring is initialized before the first forward so block zero is available.
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- This backend is single-device; distributed weight reconstruction belongs to
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- This backend uses rank-local host weights. Multi-device compute may remain
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active, but cross-rank weight reconstruction belongs to
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[Distributed Layerwise Offload](distributed_layerwise_offload.md).
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Shared selection and lifecycle behavior is defined in the
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+11
-4
@@ -2,7 +2,7 @@
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Model-level offload enforces mutual exclusion between pipeline component
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groups. For commands and model support, see the
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[model-level user guide](../../../user_guide/diffusion/offloader/cpu_offload.md).
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[model-level user guide](../../../user_guide/diffusion/offloader/module_offload.md).
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## Generic hook path
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@@ -10,10 +10,15 @@ groups. For commands and model support, see the
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makes encoders and VAEs device resident for initialization, then installs
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`SequentialOffloadHook` instances:
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- each DiT hook offloads all encoders and other DiTs before loading itself;
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- each encoder hook offloads every DiT before loading itself; and
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- each DiT hook offloads selected encoders and other selected DiTs before
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loading itself;
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- each encoder hook offloads selected DiTs before loading itself; and
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- VAEs are not part of the mutual-exclusion hook set.
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All execution-stage modules still receive a hook so an unselected resident
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stage can evict a selected component before it runs. Only components listed in
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`diffusion_offload_config.components` are eligible to move to CPU.
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The hook moves parameters and buffers without recursively calling
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`module.to()`. This avoids recursion through modules that retain references to
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other transformer blocks. Host tensors are pinned when configured; DTensors
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@@ -36,7 +41,9 @@ contract:
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- only the executing component is device resident;
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- enable and disable are idempotent;
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- transfers use the supplied device, pinning, and HSDP settings; and
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- transfers use the supplied device, pinning, and HSDP settings;
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- explicit component selection is either honored or rejected before partial
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setup; and
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- disabling removes all model-local offload hooks or contexts.
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The delegation is currently duck typed. If another independent implementation
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