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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
Signed-off-by: Hongxiao Bai <hongxiaob@nvidia.com>

* fix get shape of blockwise tensor with only compact colwise data
Signed-off-by: Hongxiao Bai <hongxiaob@nvidia.com>

* fix blockwise FP8 convert_and_update_tensor
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* for loop
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* bulk alloc
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* multi-tensor swizzle
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* pad zeros in swizzle kernels
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* unify single- and multi-tensor swizzle
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* fix empty tensor list
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* fix bug for col swizzle
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* check context & fix signifiers
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* Add basic kernel for swapping first two tensor dims
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* Add NVRTC kernel for swapping first dims
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* Tweak variable names
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* Tune kernel
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* Make sure writes are contiguous
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* Compute amax in normalization kernels as long as the pointer is provided, even if using non quantized output
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* Fuse amax computation into normalization forward
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Refactor normalization.cpp to use quantizer logic introduced in #1952 instead of manual quantization
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* Compute amax in activation kernels when the output pointer is provided, even for non-fp8 outputs
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(cherry picked from commit 9f13fe2fefc58cae93bc467d87d01ecf792a0381)

* Initialize metatensor values
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* Fuse computation of amax into the activation kernel for fp8 current scaling
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* Zero out amax in `create_hp_tensor_with_amax` instead of relying on `Float8CurrentScalingQuantizer.__init__` to zero-initialize it
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* Fix bug when constructing FP8 tensor
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* Add quantize function to C++ quantizers
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* Prototype function to coerce Python quantized tensors to match quantizer
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* Use quantizer class in tex.quantize
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* Add FP8 current scaling support for activation backward
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* Disable quantized GEMM output with FP8 current scaling
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* Add coerce_tensor functions for MXFP8 and DSv3
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* Avoid quantizing empty tensors
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* Use consistent shapes for FP8 transposes
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* In attention impl, construct FP8 tensors with pre-initialized scale-invs
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* Store copy of quantizer when creating quantized tensors
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* Fix linter warnings
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* Make sure quantized tensors have private quantizer

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* save original input
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* fix input_quantizer usage in Linear bwd
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* minor fix
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* Merge remote-tracking branch 'origin/main' into save_bf16_in_fp8_gemm
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* decouple linear bwd with save_original_input; clean up UTs
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* [PyTorch|common] Implement unpadding kernel for FP8

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

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