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

Signed-off-by: fuyue.lj <fuyue.lj@antgroup.com>
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* [PyTorch] Fuse permute+pad and unpermute+unpad ops for FP8 optimization

1.Fused `moe_permute_with_probs` + `Fp8Padding` and fused `moe_unpermute` + `Fp8Unpadding`,
  that can remove the explicit padding/unpadding of moe expert, improved performance and reduced peak gpu memory usage.
2.Add tests of fused permute/pad and unpermute/unpad.
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* [PyTorch/Common] Fuse permute+pad and unpermute+unpad support with_merging_probs
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* [PyTorch]format code
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* [Common]perf expert_idx loaded once
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* fix: pad_offsets can be None
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* add padding + merging probs bwd support. Not tested
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* Fix garbage initialized act grad
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* all test passing for jax permutation + pad
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* change tokens_per_experts APIs to num_out_tokens with conservative allocation of worst case padding for output buffer
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* change test permutation to reduce test time
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* triggering PR refresh
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* format code
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* Remove some tests cases from pytorch side. Add a separate toekn_dispatch test for sanity in case combine accidentally undo an error on dispatch in the roundtrip test. Add distinction between L0 and L2 in test cases in jax
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* format code
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* fix lint in jax
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* fix typo
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* rowwise colwise RHT group quant v1
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* remove local array RW
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* Check calling convention for amax switch.

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