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Update copyright to include 2026
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* Initial API change
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* Change all imports and api
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* Review
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* fix nvfp4 test
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[PyTorch] fix input_quantizer usage for save_original_input; fix blockwise FP8 convert_and_update_tensor (#1978)

* fix input_quantizer in save_original_input bwd
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* fix get shape of blockwise tensor with only compact colwise data
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* fix blockwise FP8 convert_and_update_tensor
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[PyTorch] FP8 Subchannel Recipe With FP8 Gather And Configurable Scaling Factor Tensor Swizzling (#1707)

* functional kernel for columnwise + no-transpose option, still hacky
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* fp8 gather pass, need minor refine
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* Add fp8_primary_weights support for blockwise scaling
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Add view to blockwise fp8 tensor
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* Add GEMM logic for blockwise quantized tensors.

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* Add license.
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* Update test gemm supported predicate.
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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.

Issues previously observed are solved.
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* Remove constexpr parameters from kernel.

Code size is reduced with fewer constexpr params.
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* Merge conflict from rebase.
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* Add shape implementations for block scaling.

nvte_shape was added upstream. Logic added
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* Remove amax_epsilon and pow_2_scales from tensor.

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