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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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* 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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* 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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* remove reciprocal op
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* Refactor Quantizer::create_tensor function
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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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* Avoid quantizing empty tensors
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* Use consistent shapes for FP8 transposes
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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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* Rename "coerce_tensor" to "convert_and_update_tensor"
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* functional passed
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Add env to chose blocklen of blockwise quantize.
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* attempt to fix misc training error
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* refactor quantize to call quantize_cpp
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* Implement separate functions for multi-tensor quantize and split + multi-tensor quantize
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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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* 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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