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

* first pass
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* Fix
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* Fix test
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* Move grad outside autocast
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* Fix kv cache
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* Change src file name in cmake
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* move the kernels too
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* fix
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* move
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* Move multi tensors kernels from PyTorch extensions to core
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* Add int16 type to core (for storing fp32 param remainders)
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* same fix to scale
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* Fix perf, memory, vars
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* Re-add device guard for multi-device
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* Fixes for test and upgrade mcore version
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* Move FP8 block scaling kernels to core
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* Blockwise float8 quantizer and quantized tensor class.

The classes are configurable for 128x128 blocksize
and 1x128 blocksize via setting block_scaling_dim == 2,1 respectively.

Scale tensors are stored in a format emenable for matrix multiplication,
however the integration of matmul is deferred as a separate story.

Fusions of quantization and DBIAS or activation functions are not yet
implemented, and the dequantization is currently implemented in torch.

Tests for quantization are included in C++ and pytorch layers, with
exact comparison to reference quantizer behavior as well as an attempt
to hit interesting branches through the API such as tensor creation
in pytorch and CPP and dequantization of row and columnwise usage.

Two CUDA kernels for quantization are included, and are direct ports
of equivalents in the kitchen repository, where a subchannel recipe
has been used for end to end training.
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* Add shape implementations for block scaling.

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* Move benchmark to te_playground
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* Remove amax_epsilon and pow_2_scales from tensor.

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* Use TypeInfo
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* MR feedback.
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* Update CS scale update test to use updated ref impl
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* Skip tests on Lovelace
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* add nvshmem based api support
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* fix lint and license issue
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