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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
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* 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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* fp8 gather pass, need minor refine
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* fix return_layernorm_output_gathered case
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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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* 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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* clean up
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* bug fix
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* relax rtol for fp32 distributed test
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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

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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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* Changed the Tensor allocation strategy
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