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* check in per-tensor current scaling full recipe
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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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* moved userbuffers code to TE/common
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* added TE/PyTorch wrappers for refactored comm+GEMM overlap code in TE/common
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* updated TE/PyTorch Python API to match the refactored comm+GEMM overlap code
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* fixing linting errors
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* added documentation for te.initialize_ub
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* fixed compile errors when building with NVTE_UB_WITH_MPI=1
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* fixed default bootstrap backend
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* switched default bootstrap backend priority to MPI > Gloo > NCCL
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* updated bootstrap backend documentation
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* close UB bootstrap socket to avoid interfering with CUDA Multicast shareable file handle send/recv
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* added torch::Tensor wrappers for communication buffer and atomic counters so PyTorch can factor externally allocated memory into its garbage collection threshold
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* automated handling of world, local and node ranks/sizes within C++ CommOverlapHelper to simplify Python function signatures
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* fixed incorrect read of environment variables
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* corrected priority for _SOCKET_IFNAME environment variables in UB bootstrapping
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* moved multicast support check to cuda_runtime.h and replaced cudaDeviceGetProp call with cached sm_count()
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* removed commented out old code and replaced external collective function type defines with aliases
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* compile-time CUDA version guard for CUDA Driver Multicast attribute
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* added compile-time CUDA version guards to Multicast code in Userbuffers
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* condensed UB docs, corrected const violations
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* fixed autodoc rst for UB calls, added CUDA version guard on Multicast UB kernels
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* fixed incorrect UB type reporting for P2P overlaps, comment reformatting
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* add docstring to tex.ubuf_built_with_mpi()
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* Update dependencies for building documentation
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* Patch RTD theme 3.0.0 to include version selector in sidebar
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* Debug printing version in sidebar
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* Fixes to Float8Tensor
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Cleanup
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* Removed cudaMalloc/WorkspaceManager in JAX csrc. JAX custom ops now request buffers from XLA for their workspace tensors.
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* Add ONNX export support for TE modules (#1)

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* fixes for pylint tests
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* refactor code by @nzmora
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* updates
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* minor fix: coding guidelines
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* fix onnxruntime version
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* minor fix: add space between code and comment
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* changes
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