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* Add GEMM logic for blockwise quantized tensors.

GEMM test cases included in pytorch integration.
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* Update NVTE_BLOCK_SCALING for GEMM.
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* Gate feature on CUDA 12.9
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* Gemm typo.
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* Reflect epilogue availability and test supported epilogues.
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* Force pow2Scales in GEMM
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* Add GEMM test to pytorch test suite.
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* Update import for GEMM test.
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* Add license.
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* Update test gemm supported predicate.
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* Use sgemm like interfaces and naming.
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* Rewrite GEMM comment.
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* MR Feedback.
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* Recipe setup for Linear modules.
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* Use 12.9 feature test.
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* Run against tensor dumps from internal library.
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* Update FIXME to TODO with linked issue.
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* Update full recompute feature to save recipe.

The recompute context uses the same recipe
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* MR Feedback. Avoid reusing quantizer objects.
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* Update logic in module.
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* Format py.
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* Update for PP bug.
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* Update test numerics.
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* Update force_power_of_2 scales in the recipe.
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* Update usage method to satisfy upstream changes.
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* fix subchannel recipe in distributed test with bf16 gather
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* Edit and cleanup BF16 gather code.
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* Update test import.
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* support columnwise only mode to 1D quantize kernel
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* Format and move enum
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* Skip alloc.
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* try async bf16 gather
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* Format python code.
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* Document and type code.
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* Update pytorch lint errors.
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* Dont set high precision dtype.
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* Add test for sanity and CG; fix CG for sequential?
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* Keep make_quantizers API stable

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* Fix import name.
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* refactor for nvte_quantize_v2
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* Test fp32 scales.
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* Simplify layernorm linear
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* LayerNorm linear bwd gather logic.
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* Communication updates.
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* Update transformer_engine/pytorch/ops/op.py

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* Lint fix.
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* MR feedback.
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* Enable cuda graph tests.
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* Reduce chance of spurious failure and reword.
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* Update CPP tests.
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* fix cpp warning
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* more fix
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* refactor to add cp support for sbhd/bshd
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* merge sbhd/bshd and thd functions
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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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* Apply linting changes.
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* Alignment for 1D scaling for GEMM edge case.
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* MR feedback.
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* Change API name.
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* Fix merge conflict with name change.
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* Use common tensor map API.
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* Change API to use two scaling mode enums.
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* Fix typo.
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* Update some call sites.
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* Tests for torch tensor API surface.

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* Reuse scale calculation between quantizer refs.
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* Save memory by dropping reference to saved tensors.

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* Remove constexpr parameters from kernel.

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* Merge conflict from rebase.
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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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* Lint changes.
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* Safer ifdef in kernel.
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* Reuse compute_scale function from Current Scaling.
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* Bugfix on inf_value scale refactor.
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