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Spatially Distributed Fast Bottleneck block

* Added support for fused ReLU and dropout into transducer joint

* Reorganized code selection path in transducer joint fwd
* Added support for fused ReLU+dropout into transducer joint

* Vectorize transducer loss backward with fused softmax (#3)

* Nanz/transducer loss (#4)

* Vectorize transducer loss backward with fused softmax

* Added a predicate to avoid potential IMA

* Nanz/transducer loss (#5)

* Vectorize transducer loss backward with fused softmax

* Added a predicate to avoid potentional IMA

* Added more predicates to avoid IMAs

* Updated documentations for newly added features.

* Fixed a error in transducer.py

* local_rank and install cuda version fix

update fusedadam docstring

* clip before reduce scatter

* provide clip before/after RS option

* change to clip after ar (avoid confusion)

* fix comments

Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* don't create cublasLt handle, fix zero block size case

* cleanup

* initial cublaslt support

* 64 bit input

* add license headers

* cleanup

* remove license
Co-authored-by: pbialecki <pbialecki@nvidia.com>

* initial commit for adding fast bottleneck

* sync cudnn-frontend module
Co-authored-by: pbialecki <pbialecki@nvidia.com>

Fixed a typo

* enable no_copy

* barrier for SHARP

* set verbose=False by default
Co-authored-by: Kexin Yu <kexiny@nvidia.com>

* Add unit tests for fused-novograd

* Fix: tensors should reside on the same device

* Fix: Cudastream should be called on the same device on which the tensors reside on. Found this during debugging fused novograd multi-device unit test

* fixed issues mentioned in the comments

* sync free Distributed LAMB

* init lr with provided value

* wait l2 norm strem

* reorder param

* fix indent
Co-authored-by: Kexin Yu <kexiny@nvidia.com>

* Initial check-in of the transducer extension.

* Added more comments to help explain the code

* Corrected minor typos

* 1. Renamed variable in tests to match the extension
2. Disabled ninja build option

* copy-paste friendly

* fix import container_abcs issue

Nightly PyTorch has removed `container_abcs` from `torch._six`.  https://github.com/pytorch/pytorch/commit/58eb23378f2a376565a66ac32c93a316c45b6131#diff-b3c160475f0fbe8ad50310f92d3534172ba98203387a962b7dc8f4a23b15cf4dL35

* fix import container_abcs issue

Nightly PyTorch has removed `container_abcs` from `torch._six`.
https://github.com/pytorch/pytorch/commit/58eb23378f2a376565a66ac32c93a316c45b6131#diff-b3c160475f0fbe8ad50310f92d3534172ba98203387a962b7dc8f4a23b15cf4dL35

* keep existing for pytorch1.7 and earlier

Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

Update ASP README to highlight default recipe

The Recipe was presented after some non-standard API calls, so moving the suggested usage up, giving it its own section, and reinforcing the suggested usage in the non-standard section.

* add flag for DistributedAdam: step_support_amp_scaling
Co-authored-by: Kexin Yu <kexiny@nvidia.com>
Co-authored-by: Kexin Yu <kexinznzn›@gmail.com>

* fuse dropout into softmax in fprop for additive mask case

- resume() is a nested function and when it loads best_prec1
  it creates a local variable that hides the one from the parent
  function (which refers to the global one). This PR adds `global`
  to modify the global variable as intended
Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>

DistributedFusedAdam Model Parallelism Support (Megatron)
Co-authored-by: Kexin Yu <kexiny@nvidia.com>
Co-authored-by: Kexin Yu <kexinznzn›@gmail.com>

In this PR, we mainly tried to optimize the performance of Syncatchnorm and also fixed one potential issue in the welford_parallel kernel implementation.

For performance improvement, we batched the mean/var/count all_gather communication together and sent it once in the forward path
We also batch the all_reduce in backward path
We add the contiguous call on the input of welford_parallel kernel.
If there is any standard perf benchmark, I would be happy to run it.

Update asp readme

Update README for ASP

Added an outline to illustrate our recommended recipe to obtain a pruned model

Co-authored-by: pbialecki <pbialecki@nvidia.com>

* add device guards to the optimizers

* add untracked file

* set deviceGuard in multi_tensor_apply

* address review comments; fix lamb

* indent

* typo