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* update * update * make style * remove dynamo disable * add coauthor Co-Authored-By: Dhruv 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: Steven 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: Dhruv 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: Dhruv Nair <dhruv.nair@gmail.com> Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>