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* Updating BLOCK_SIZE to 1024.
tests/L0/run_optimizers/test_fused_optimizer.py test passes except for bfloat16 for Adam. There seems to be a bug in this test that needs to be resolved.
For now skipping test_bfloat16 for Adam in the unittest.
Ran 17 other tests and ALL other tests pass!
More details on the effects of these changes can be found here -  https://confluence.amd.com/display/MLSE/Apex+Kernel+Optimization

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This commit changes BLOCK_SIZE=1024 ONLY FOR different optimizers.
L2norm kernels (part of LAMB optimizer algorithm) still maintain BLOCK_SIZE=512 otherwise Allclose fails.

* Updating tests/L0/run_optimizers/test_fused_optimizer.py with @skipifRocm to skip test_bfloat16 in Adam.
Co-authored-by: aspanday <aspanday@amd.com>

This reverts commit bdd481d1.

* 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...

* modify MTA axpby for wider load/store

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

* change .type().ScalarType() to .scalar_type() + at::ScalarType::X to at::kX

* revert scalar_type() to type() for AT_DISPATCH_FLOATING_TYPES_AND_HALF

* revert scalar_type() to type() in AT_DISPATCH_FLOATING_TYPES

* revert scalar_type() to type() for AT_DISPATCH_FLOATING_TYPES_AND_HALF in welford.cu

* revert scalar_type() to type() in layer_norm_cuda_kernel.cu

* revert at::kType  to at::ScalarType::Type

* use DISPATCH_FLOAT_AND_HALF to get rid of warnings

* add dispatch mechanisms for double+float and double+float+half

* Refactor to allow more flexible treatment of multiple optimizers/models/losses

* Adding _process_optimizers.py

* Created L0 tests (now passing).

* fix: minor print typo (#234)

* make L1 results easier to read

* L0 multiple model/optimizer/loss test fleshed out

* Adding test that master params remain synced across distributed processes

* Docstring updates

* Docstring updates

Allow multi-tensor unscale to handle FP16 output, so it can also be used for copy-scatter. Rename some options.