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* check in per-tensor current scaling full recipe
Signed-off-by: zhongboz <zhongboz@nvidia.com>

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setup basics of current scaling quantizer in python level
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add test case for current scaling dequantize
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finish linear layer fwd bwd test, determined error with bf16
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achieved zero tolerance for Linear by specify gemm use_split_accumulator config
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enable layernormlinear with current scaling, pass bitwise test
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refactor test case code
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make current scaling quantizers distrbuted, pass distributed linear&layernormlinear tests
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bug fix: use cached fp8 recipe in backward
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fix layernorm_mlp with current scaling, fix activation_helper with current scaling
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support detailed numerical settings from recipe to quantization kernel
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resolving MR comments
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recipe naming
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* resolve mr comments, remove IS_CURRENT_SCALING template from kernels
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* resolve mr comments, make current scaling c++ test cases
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* add current scaling to test_numerics.py, skip act recomp and grouped linear
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* add benchmark for quantizer
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* add benchmarks for linear layer
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* bug fix, typo
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* resolve more mr comments
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* avoid potential race condition by not using from_blob to construct amax tensor in C++
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* resolve more comments
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* Debug linter warnings and license check
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* Debug import error in FP8 tensor test
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* Debug compilation error with CUDA 12.1 for Turing
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* resolve mr comments, fix activation cast fusion
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* resolve comments, add NVTEQuantizationParams for compute scale
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* remove is_current_scaling check totally from common folder
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* remove benchmarks, will contribute in another repo
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* adjust cs default recipe config
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* adjust comments in test
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* Remove current scaling mode from core lib
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* Refactor current-scaling-specific logic in core C++ lib

Move amax and scale update functions out of casting functions, and put into dedicated current-scaling source file. Add general API for accessing quantization config object.
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* Add missing header in C++ tests
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* Disable test config with FP8 transpose on Blackwell
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* Fix compilation error in C++ test
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* Fix file extensions
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* fix build
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* upgrade paddle container for CI
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* Apply formatting
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* Apply formatting
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* Avoid select operation in cast-transpose extension
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* Avoid select operation in cast-transpose-dbias extensions
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* Avoid select op in LayerNorm and RMSNorm
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* Fix linter errors
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* Exposing RMSNorm in pyTorch extensions
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* First pass at the Python API
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* Small fixes
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* Added numerics tests and fixed issues
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* Lint fixes
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* Added RMSNorm to LayerNormMLP
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* Added ONNX export and tests for RMSNorm
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* Fix python lint
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* Fix BERT case
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* Added normalization option to the TransformerLayer
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* Fix documentation
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* Fix kwarg bug
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* Fix IMA and invalid type error
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* Increase RMSNorm threshold for bf16 case
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* Fix ONNX tests
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* Add FP8 fused attention to TE for PyTorch
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* add license for cudnn-frontend, modify installation requirements, and refactor some headers for aesthetics
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* add c api docs for fused attention
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* add exception for unsupported precision/sequence length combinations
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* fix installation requirement for non fused attn use cases
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* fix docs for fused-attn
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* prefix enums with NVTE_ and replace old MHA_Matrix with NVTE_QKV_Matrix
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* minor fixes based on PR comments
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* fix description for kvpacked fwd
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* fix description of Bias in C api
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* minor fixes for cudnn requirement and description for QKV tensors
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* fix QKV layout description and support matrix for C api
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* add asserts to cpp_extensions for qkv layout/bias type/attn mask type
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* fix typo precision
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* Move the amax/scale/scale_inv into the TE Tensor struct.
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* Handle multi_cast_transpose
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* Changed softmax to new Tensor
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* First pass at the cpp tests
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* Round of fixes
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* Fix multi_cast_transpose
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* Fix cast_to_fp8
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* Add kernel for multi-tensor cast-transpose
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* Fix incorrect test function in multi-tensor cast-transpose unit test
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* Remove std::vector from multi-tensor cast-transpose function signature

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* Remove fp8_out from LN API
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* fix LN test
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* Fixes
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