GitLab

Store module extra state in tensor
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Handle deprecated `hidden_size` arg in norm modules
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* Support initializing norm ops on CPU
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* Add integration test for Megatron-LM
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* Handle case in RMSNorm where hidden dim is not provided
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* Add activation ops
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* Fix lint warnings
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* Fix linter warning
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* Update to use QuantizedTensor
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* Respect PyTorch autograd dtype
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* Rename CastFloat8 op to Quantize
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* Add support for fused dSwiGLU-cast-transpose
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* Remove manual FP8 scale update for FP8 params
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* lint
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Debug ONNX export with te.Sequential

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Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Add Userbuffers support for column TP linear layer
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* Add Userbuffers support for row TP linear layer
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* Interpret linear+RS as row TP linear
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* Add Userbuffers support for FP8 row TP linear layer

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* Debug bug with incorrect bias pointers in UB GEMM

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* Add Userbuffers support for linear dgrad

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Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Add Userbuffers support for linear wgrad
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* Add support for grad bias
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* Fused cast-transpose-dbias
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* Support case where wgrad is optional
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* Expand documentation
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* Fix linter warnings
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* Use recently added convenience functions in Float8Tensor
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* Respect autograd dtype
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* Fix missing imports
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* Respect PyT autocast dtype in bprop
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* Fix linter warnings
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* Debug merge conflicts
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* Add layer norm op
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* Add FP8 cast op
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* Add tests for linear and layernorm with FP8 output
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* RMSNorm op
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* Fix linter warnings
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* Replace LayerNorm module with LayerNorm op
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* Replace RMSNorm module with RMSNorm op
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* Add AMP support
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* Do not save autograd context if grad mode is disabled

Debugging ONNX export tests.
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* Forward args in pre_forward func to base op class
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* Update to use QuantizedTensor class
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* Apply suggestions from code review
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* Review suggestions from @ptrendx

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* Fix linter warnings
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* Use weight dtype as default compute dtype
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* Fix linter warnings
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* Debug checkpointing with operation-based API
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* Store checkpoint FP8 state on CPU
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* Fix bug where linear op was saving params multiple times
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* Fix linter warnings
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---------
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* Debug CUDA graph support with operation-based API
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* Refactoring CUDA graph tests
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* Review suggestions from @ptrendx

Return default recipe from FP8GlobalStateManager.get_fp8_recipe if needed. Expand error message when failing to load FP8 state after capturing CUDA graph.
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* Avoid unnecessary recursion when saving/loading FP8 state
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* Fix circular import
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* Upgrade pylint and first round formatting
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* round 2
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* Format and fixes
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* Fixes
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* Handle Float8Tensor when casting module dtype

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* Respect autocast dtype in linear op
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* Suppress linter warning
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* Suppress linter warning
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* Tweak comments

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* Add base class for tensor proxies
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* Move tensor detaching logic to tensor proxy base class
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* Use Python wrappers to PyTorch extensions
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* Include transpose caching logic in proxy encode function
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* Debug dimension mismatch with amax history
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* Move dequantize logic to proxy_decode func
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* Rename to "QuantizedTensor"
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* Rename "proxy_detach" to "detach"
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* Include transpose cache in detach and clone funcs
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* Fix linter warnings
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* Update FP8 workspaces with QuantizedTensor functions
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* Move logic for FP8 transpose cache in FP8 workspaces to base class
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* Remove cast-transpose logic from linear op
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* Remove unnecessary args for Float8Tensor when using FP8 attr dict
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* Remove __torch_function__ to QuantizedTensor
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* Fix linter warnings
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* Update tests/pytorch/test_float8tensor.py
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* Debug FP8 transpose test
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* Debug cast functions
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* Add op for in-place add
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* Add op for in-place add
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* Add op that adds extra output to fuser
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* Add fused op for GEMM+bias+add
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* Add fused op for dgrad+add
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* Add documentation
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* Fix linter warnings
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* Review suggestions from @ptrendx

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* Debug test failures
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* Link attention docs to the main docs and fix errors reported by Sphinx
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* Lower the version of nbsphinx
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* More fixes
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* Change the URL of example_attention.py to GitHub
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* More fixes in the attention tutorial
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* Update sequential container constructor to handle modules in plain dicts
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* Avoid initializing Sequential with dicts
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---------
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* Add basic infrastructure for Sequential module
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* Add FP8 support in linear op

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* Add reshape op and unit test
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* Debug unfused linear op
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* Consolidate unfused ops in submodule
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* Disable GEMM+bias fusion with FP32 activations

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* Refactor parallel tests to reduce job launches
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* Remove unused file
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* Add license boilerplate
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* Fix cuDNN front-end commit
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* Use updated FP8 tensor API for transpose caching
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* Initial functional implementation of linear op
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* Rename subdirectory from "fuser" to "ops"

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* Update with Float8Tensor changes in #820
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