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* Added support of FP4 data type
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* Refactoring to BitsNum in progress
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* Fixed compilation errors. All C++ tests passed
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* Fixed a typo
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* Fixed errors in JAX C++ extensions
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Use ocp fp8.
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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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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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* resolve mr comments, remove IS_CURRENT_SCALING template from kernels
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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 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

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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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* Perform scale-inv update in cast-transpose kernels
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* Perform scale-inv update in cast and activation kernels
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* Simplify kernel to update FP8 scale-inv
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* Apply formatting
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* Apply formatting
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* Merged CT+dbias+dact into a single template
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* Moved gated activations ifrom the cast_transpose_fused ito a sseparate cpp file
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* Code clean up
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* Code clean up
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* Code clean up
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* Update transformer_engine/common/transpose/cast_transpose_fusion.cu
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* Update transformer_engine/common/transpose/cast_transpose_fusion.cu
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* Reverted the change with the file split
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* Implemented JIT compiled kernels
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* Code clean up
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* Replaced aligned statically compiled kernels with JIT kernels. Added support of various activations functions for JIT kernels. Cleaned up the code per the code review
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* Code clean up
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add comments
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* Initial implementation of NVRTC infrastructure
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