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Change python3.6 to python

This reverts commit 964e61f1.

* run backward

* remove custom_fwd/custom_bwd

* [ColumnParallelLinear] Test behavior in autocast

* fix test

* casts manually to autocast dtype

Co-authored-by: Piotr Bialecki <pbialecki@nvidia.com>
Co-authored-by: Eddie Yan <eddiey@nvidia.com>
Co-authored-by: Rishi Puri <riship@nvidia.com>
Co-authored-by: Sangkug Lym <slym@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

use __launch_bounds__(1024) for multi_tensor_apply, re-enable skipped tests

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

* fix warp size in WARP_SHFL* in layernorm

* enable fused_layer_norm tests on ROCm

* enable mlp cuda

* add setup changes and tests

* skip the unit tests

* updated conditions for empty array

* removed hip platform conditions

* add device guards to the optimizers

* add untracked file

* set deviceGuard in multi_tensor_apply

* address review comments; fix lamb

* indent

* typo

* fix dropout scaling from p to 1/(1-p) (#816)
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* Improvements to apex.mlp (#804)

* update fused bias relu backward kernel

* adding support for not require first layer dgrad

* fix bug: wrong layer in requires grad

* add infrastructure for optional bias and activation, currently only support no bias and no relu

* make bias and relu optional separately

* add sigmoid activation option

* enable wider load/store for multi_tensor_apply kernels (#763)

* modify MTA axpby for wider load/store

* Make scale/axpby/l2/adam/lamb multi_tensor uses wider load

* Changes to make xentropysoftmax load/store vectorized when possible: (#725)

* Changes to make xentropysoftmax load/store vectorized when possible:
Increase default ILP so that each thread handle 16 Bytes data in one step
Make thread load/store longest vector possible
Make unroll case handle adjacent data instead of strided, so same order compare to vector case

* Add shift for not aligned case. Remove less than 16 bytes aligned access
Co-authored-by: Burc Eryilmaz <sberyilm@gmail.com>
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>
Co-authored-by: Deyu Fu <deyuf@nvidia.com>

* update fused bias relu backward kernel

* adding support for not require first layer dgrad

* fix bug: wrong layer in requires grad

* add infrastructure for optional bias and activation, currently only support no bias and no relu

* make bias and relu optional separately

* add sigmoid activation option

The LARC optimizer wraps an underlying optimizer and then needs to be passed
to amp.initialize for mixed precision. There were 3 different crashes happening
in this situation, fix all of them and add a unit test.

I don't know if the 'LARC' in sys.modules check ever worked. In my setup, the
entry in sys.modules is 'apex.parallel.LARC'. Checking if the variable is
defined seems more reliable though.