GitLab

* Implemented deferred initialization via `device='meta'` option for te.Linear and added new PyTorch example to demonstrate its use with FullyShardedDataParallel execution.
Signed-off-by: Alp Dener <adener@nvidia.com>

* correcting Float8Tensor initialization and fixing linting errors
Signed-off-by: Alp Dener <adener@nvidia.com>

* removed duplicate code from upstream rebase, local tests passing
Signed-off-by: Alp Dener <adener@nvidia.com>

* improved comments/documentation for FSDP example
Signed-off-by: Alp Dener <adener@nvidia.com>

* converted reset_parameters() into a base module function
Signed-off-by: Alp Dener <adener@nvidia.com>

* fixed Float8Tensor creation with deferred init, all tests passing locally
Signed-off-by: Alp Dener <adener@nvidia.com>

* extended deferred initialization to all TE modules
Signed-off-by: Alp Dener <adener@nvidia.com>

* fixed linting errors
Signed-off-by: Alp Dener <adener@nvidia.com>

* removed unnecessary reference to the parent module of parameter, added clarifying comments in parameter reset
Signed-off-by: Alp Dener <adener@nvidia.com>

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Signed-off-by: Alp Dener <adener@nvidia.com>

Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

* [PyTorch] Linear: fix computation for wgrad if sequence_parallel=True
Signed-off-by: Markus Schnoes <markus.schnoes@gmx.de>

* Remove buggy gather_along_last_dim
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* [PyTorch] Linear: fix line length
Signed-off-by: Markus Schnoes <markus.schnoes@gmx.de>

* Simplify logic for saving input tensor for Linear backward
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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Signed-off-by: Markus Schnoes <markus.schnoes@gmx.de>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Co-authored-by: Tim Moon <tmoon@nvidia.com>

Improve PyTorch memory usage
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Debug PyTorch and Paddle tests on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Only run Paddle layer tests with cuDNN fMHA on supported archs
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Debug PyTorch fMHA tests
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Reduce JAX FP8 GEMM sizes

Avoid split-k kernels on Ada.
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Disable JAX fused self-attention test on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Run supported fused attention tests on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Run supported fused attention JAX tests on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Enable Paddle fused attention on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Update reference scale calculation in TensorFlow test
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Restore backend support to reference FP8 attention impl in PyT test

Review suggestion from @cyanguwa
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Fix merge conflicts
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Debug Paddle tests on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Loosen tolerances for Paddle attention tests
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Assume causal mask implies equal seqlens in Paddle attention tests
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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

* Better dimension assert for FP8
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* line
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Clearer error messages for dtype and shape assertions
Signed-off-by: Carlos Mocholí <carlossmocholi@gmail.com>

* Update transformer_engine/pytorch/utils.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Carlos Mocholí <carlossmocholi@gmail.com>

* Update transformer_engine/pytorch/utils.py
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Carlos Mocholí <carlossmocholi@gmail.com>

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Signed-off-by: Carlos Mocholí <carlossmocholi@gmail.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Catch FP8 modulo16 error before cublas and fp8 kernels
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* annotate
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* ignore self attention mask for causal type
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* further relax checks to run FA, update docs
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* fix pytorch softmax path
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* fixes
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* minimum ampere requirement for fa
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* fix qkv weight unfused path
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* fix non FA non interleaved case
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

retain grad related attrs while casting
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

Signed-off-by: Przemek Tredak <ptredak@nvidia.com>
Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Przemek Tredak <ptredak@nvidia.com>