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Update copyright to include 2026
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

[PyTorch] FP8 Subchannel Recipe With FP8 Gather And Configurable Scaling Factor Tensor Swizzling (#1707)

* functional kernel for columnwise + no-transpose option, still hacky
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* pass all quantizer unit tests
Signed-off-by: zhongboz <zhongboz@nvidia.com>

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* refactor, add gemm ready api
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* make format options private members, simplify api
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* swizzle scales right before gemm
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* bug fix of single layer test
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* attempt to fix lint issue
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* fp8 gather pass, need minor refine
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* fix return_layernorm_output_gathered case
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* remove special cases, add sanity check before gemm
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* fix lint
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* lint ungrouped imports
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* Implement dequantize for compact 1D blocks.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* add more unit test with dequantize compact supported
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* lint again
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* make ag for subchannel respect async
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* zero tolerance in distributed test
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* fix zero tolerance test
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* resolve rebase issues
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* lint & format
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* fix lint
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* clean up
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* bug fix
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* relax rtol for fp32 distributed test
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* fix some ci issue
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* fix ci test failure in debug mode
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* Force row-wise and column-wise data to have same data format

Prototype "all-gather usage" in quantizer.
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* Remove dead logic for high-precision AGs
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* Debug FP8 block-wise tensor tests
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* Debug distributed test
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* Handle case where LayerNormLinear returns gathered norm output
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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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* Remove unecessary type converter change.
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* Reflect epilogue availability and test supported epilogues.
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* GEMM simplifications from recipe branch.
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* Format py code.
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* Update GEMM DGelu tests to match support depending on output dtype.
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* Force pow2Scales in GEMM
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* Add GEMM test to pytorch test suite.
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* Rewrite GEMM comment.
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* MR Feedback.
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* Refactor GEMM param canonicalization

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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 amax_epsilon and pow_2_scales from tensor.

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* Reuse compute_scale function from Current Scaling.
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* Add copyright to reference scale calc.
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* Use ptx.cuh functions instead of cde.
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* Use TypeInfo
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* MR feedback.
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* Skip tests on Lovelace
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* Enable fp8_primary_weights for current scaling
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* Use different cast_master_weights_to_fp8 functions depending on the type of quantizer
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* All amaxes of model_weights should participate in reduce-max
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enable layernormlinear with current scaling, pass bitwise test
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support detailed numerical settings from recipe to quantization kernel
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* add benchmark for quantizer
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* resolve more comments
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