* Fix TaylorSeer cache crash when the feature shape changes between steps
* Add QwenImage 2.1 x TaylorSeer tests covering the KV-cache prefill shape change
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Add optional FP32 sampling state for Cosmos3
* Add cache execution contexts to Cosmos3 pipelines
* Add SeaCache support for Cosmos3
* Cap consecutive SeaCache reuses
* Align SeaCache defaults with Cosmos3 inference
* Enable SeaCache by default for Cosmos3
* Document SeaCache for Cosmos3
* Use FP32 sampling state by default for Cosmos3
* Fix SeaCache reconfiguration, transfer state isolation, and FP32 sampling
* Remove SeaCache stats and ablation instrumentation
* Move SeaCache to transformer-level and disable by default
* Align SeaCache and tests with model-level cache patterns
* Make Cosmos3 sampling state always FP32
* Add pipe definition to the docs snippet
* Remove redundant hook registry cache resets
* Use raw vision latents for SeaCache indicators
* Make Cosmos3 SeaCache safe for regional compilation
* Fix SeaCache with Cosmos3 model parallelism
* Support single-stream models in SeaCache
* Add Wan T2V support to SeaCache
* pass denoising step info to cache hooks via cache_context
Replaces SeaCache's pipeline-state callbacks: the pipeline attaches the loop
coordinates (step_index, sigma, num_inference_steps) to the denoiser call with
`cache_context(name, ...)`, carried by a frozen CacheContext dataclass and stored
on each stateful hook's StateManager (the registry no longer holds context).
Co-Authored-By: Dhruv Nair <dhruv.nair@gmail.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* Fix Wan cache metadata and Cosmos modular cleanup
Pass denoising step metadata to Wan T2V conditional and unconditional cache contexts.
Restore stateful cache reset after Cosmos modular pipeline calls, restore the required Literal import, and update regression tests and documentation.
* Refactor Cosmos3 SeaCache decoder hook
* Apply CI formatting updates
* Fix distilled system prompt fallback
* Preserve Cosmos3 fp32 sampling state
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Co-authored-by: yiyixuxu <yixu310@gmail.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* Invalidate HookRegistry child-registries cache on enable/disable cache
* Test the enable-cache-after-inference flow through a pipeline
* Make the FirstBlockCache regression test drive two explicit cache_context forwards
* Drop the pipeline-level enable-cache-after-inference test
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* refactor magcache tests.
* include taylorseer in the caching mixin.
* up
* add back magcache and migrate to pytest
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Co-authored-by: dg845 <58458699+dg845@users.noreply.github.com>
* Implement synchronous onload for offloaded parameters
Add fallback synchronous onload for conditionally-executed modules.
* add test for new code path about group-offloading
* Update tests/hooks/test_group_offloading.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* use unittest.skipIf and update the comment
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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
* fix: group offloading to support standalone computational layers in block-level offloading
* test: for models with standalone and deeply nested layers in block-level offloading
* feat: support for block-level offloading in group offloading config
* fix: group offload block modules to AutoencoderKL and AutoencoderKLWan
* fix: update group offloading tests to use AutoencoderKL and adjust input dimensions
* refactor: streamline block offloading logic
* Apply style fixes
* update tests
* update
* fix for failing tests
* clean up
* revert to use skip_keys
* clean up
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Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
* update
* fix
* non_blocking; handle parameters and buffers
* update
* Group offloading with cuda stream prefetching (#10516)
* cuda stream prefetch
* remove breakpoints
* update
* copy model hook implementation from pab
* update; ~very workaround based implementation but it seems to work as expected; needs cleanup and rewrite
* more workarounds to make it actually work
* cleanup
* rewrite
* update
* make sure to sync current stream before overwriting with pinned params
not doing so will lead to erroneous computations on the GPU and cause bad results
* better check
* update
* remove hook implementation to not deal with merge conflict
* re-add hook changes
* why use more memory when less memory do trick
* why still use slightly more memory when less memory do trick
* optimise
* add model tests
* add pipeline tests
* update docs
* add layernorm and groupnorm
* address review comments
* improve tests; add docs
* improve docs
* Apply suggestions from code review
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
* apply suggestions from code review
* update tests
* apply suggestions from review
* enable_group_offloading -> enable_group_offload for naming consistency
* raise errors if multiple offloading strategies used; add relevant tests
* handle .to() when group offload applied
* refactor some repeated code
* remove unintentional change from merge conflict
* handle .cuda()
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Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
