Commit Graph

9 Commits

Author SHA1 Message Date
psychedelicious
38343917f8 fix(backend): revert non-blocking device transfer
In #6490 we enabled non-blocking torch device transfers throughout the model manager's memory management code. When using this torch feature, torch attempts to wait until the tensor transfer has completed before allowing any access to the tensor. Theoretically, that should make this a safe feature to use.

This provides a small performance improvement but causes race conditions in some situations. Specific platforms/systems are affected, and complicated data dependencies can make this unsafe.

- Intermittent black images on MPS devices - reported on discord and #6545, fixed with special handling in #6549.
- Intermittent OOMs and black images on a P4000 GPU on Windows - reported in #6613, fixed in this commit.

On my system, I haven't experience any issues with generation, but targeted testing of non-blocking ops did expose a race condition when moving tensors from CUDA to CPU.

One workaround is to use torch streams with manual sync points. Our application logic is complicated enough that this would be a lot of work and feels ripe for edge cases and missed spots.

Much safer is to fully revert non-locking - which is what this change does.
2024-07-16 08:59:42 +10:00
Ryan Dick
1d449097cc Apply ruff rule to disallow all relative imports. 2024-07-04 09:35:37 -04:00
Ryan Dick
414750a45d Update calc_model_size_by_data(...) to handle all expected model types, and to log an error if an unexpected model type is received. 2024-07-04 09:08:25 -04:00
Lincoln Stein
a3cb5da130
Improve RAM<->VRAM memory copy performance in LoRA patching and elsewhere (#6490)
* allow model patcher to optimize away the unpatching step when feasible

* remove lazy_offloading functionality

* allow model patcher to optimize away the unpatching step when feasible

* remove lazy_offloading functionality

* do not save original weights if there is a CPU copy of state dict

* Update invokeai/backend/model_manager/load/load_base.py

Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>

* documentation fixes requested during penultimate review

* add non-blocking=True parameters to several torch.nn.Module.to() calls, for slight performance increases

* fix ruff errors

* prevent crash on non-cuda-enabled systems

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
2024-06-13 17:10:03 +00:00
Ryan Dick
829b9ad66b Add a callout about the hackiness of dropping tokens in the TextualInversionManager. 2024-05-28 05:11:54 -07:00
Ryan Dick
3aa1c8d3a8 Update TextualInversionManager for compatibility with the latest transformers release. See https://github.com/invoke-ai/InvokeAI/issues/6445. 2024-05-28 05:11:54 -07:00
Ryan Dick
994c61b67a Add docs to TextualInversionManager and improve types. No changes to functionality. 2024-05-28 05:11:54 -07:00
psychedelicious
5a3195f757 final tidying before marking PR as ready for review
- Replace AnyModelLoader with ModelLoaderRegistry
- Fix type check errors in multiple files
- Remove apparently unneeded `get_model_config_enum()` method from model manager
- Remove last vestiges of old model manager
- Updated tests and documentation

resolve conflict with seamless.py
2024-03-01 10:42:33 +11:00
Lincoln Stein
5d612ec095 Tidy names and locations of modules
- Rename old "model_management" directory to "model_management_OLD" in order to catch
  dangling references to original model manager.
- Caught and fixed most dangling references (still checking)
- Rename lora, textual_inversion and model_patcher modules
- Introduce a RawModel base class to simplfy the Union returned by the
  model loaders.
- Tidy up the model manager 2-related tests. Add useful fixtures, and
  a finalizer to the queue and installer fixtures that will stop the
  services and release threads.
2024-03-01 10:42:33 +11:00