1. 29 Jan, 2025 2 commits
  2. 28 Jan, 2025 4 commits
  3. 27 Jan, 2025 9 commits
  4. 26 Jan, 2025 1 commit
  5. 24 Jan, 2025 3 commits
  6. 23 Jan, 2025 7 commits
  7. 22 Jan, 2025 3 commits
    • Dhruv Nair's avatar
      [CI] Update HF_TOKEN in all workflows (#10613) · 8d6f6d6b
      Dhruv Nair authored
      update
      8d6f6d6b
    • Aryan's avatar
      Improve TorchAO error message (#10627) · ca60ad8e
      Aryan authored
      improve error message
      ca60ad8e
    • Aryan's avatar
      [core] Layerwise Upcasting (#10347) · beacaa55
      Aryan authored
      
      
      * update
      
      * update
      
      * make style
      
      * remove dynamo disable
      
      * add coauthor
      Co-Authored-By: default avatarDhruv Nair <dhruv.nair@gmail.com>
      
      * update
      
      * update
      
      * update
      
      * update mixin
      
      * add some basic tests
      
      * update
      
      * update
      
      * non_blocking
      
      * improvements
      
      * update
      
      * norm.* -> norm
      
      * apply suggestions from review
      
      * add example
      
      * update hook implementation to the latest changes from pyramid attention broadcast
      
      * deinitialize should raise an error
      
      * update doc page
      
      * Apply suggestions from code review
      Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>
      
      * update docs
      
      * update
      
      * refactor
      
      * fix _always_upcast_modules for asym ae and vq_model
      
      * fix lumina embedding forward to not depend on weight dtype
      
      * refactor tests
      
      * add simple lora inference tests
      
      * _always_upcast_modules -> _precision_sensitive_module_patterns
      
      * remove todo comments about review; revert changes to self.dtype in unets because .dtype on ModelMixin should be able to handle fp8 weight case
      
      * check layer dtypes in lora test
      
      * fix UNet1DModelTests::test_layerwise_upcasting_inference
      
      * _precision_sensitive_module_patterns -> _skip_layerwise_casting_patterns based on feedback
      
      * skip test in NCSNppModelTests
      
      * skip tests for AutoencoderTinyTests
      
      * skip tests for AutoencoderOobleckTests
      
      * skip tests for UNet1DModelTests - unsupported pytorch operations
      
      * layerwise_upcasting -> layerwise_casting
      
      * skip tests for UNetRLModelTests; needs next pytorch release for currently unimplemented operation support
      
      * add layerwise fp8 pipeline test
      
      * use xfail
      
      * Apply suggestions from code review
      Co-authored-by: default avatarDhruv Nair <dhruv.nair@gmail.com>
      
      * add assertion with fp32 comparison; add tolerance to fp8-fp32 vs fp32-fp32 comparison (required for a few models' test to pass)
      
      * add note about memory consumption on tesla CI runner for failing test
      
      ---------
      Co-authored-by: default avatarDhruv Nair <dhruv.nair@gmail.com>
      Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>
      beacaa55
  8. 21 Jan, 2025 6 commits
  9. 20 Jan, 2025 2 commits
  10. 19 Jan, 2025 2 commits
  11. 16 Jan, 2025 1 commit