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

Signed-off-by: wenjh <wenjh@sugon.com>

Add env to chose blocklen of blockwise quantize.
Signed-off-by: wenjh <wenjh@sugon.com>

Fix pytest of blockwise error
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Resolve new api in  int8 gemm test
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Fix incorrect launch parm
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Fix 1D blockwise(64) acc error
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Same intention of commit 3e38a2ea

.
This commit is to improve acc.
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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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* pass all quantizer unit tests
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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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* Implement dequantize for compact 1D blocks.
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* add more unit test with dequantize compact supported
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* Force row-wise and column-wise data to have same data format

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* Add GEMM logic for blockwise quantized tensors.

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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.

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to hit interesting branches through the API such as tensor creation
in pytorch and CPP and dequantization of row and columnwise usage.

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has been used for end to end training.
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