GitLab

* Add dropout kernel with 8-bit RNG
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* Fix license
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* Avoid ambiguous types
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* Do not enforce dropout prob is representable in 8 bits
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* Expand error message
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* Fix small statistical bug from using less-equal instead of less-than

Refactor kernel implementations and add comments. Interpret masks as bytes rather than 16-bit uints.
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* Fix linter warning
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* Remove unnecessary helper function in PyTorch extensions
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[PyTorch][MOE] Tentative Fix For Replacing from_blob with empty for experts receiving zero tokens (#2134)

use torch empty for empty shape instead of from_blob
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expose more activation functions
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* Add rmsnorm_bwd_add
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* Add BackwardAddRMSNorm fused operation
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* Try to optimize register usage in kernels
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* Add separate BackwardAdd stage for the fused backward add
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* Add `nvte_cublas_gemm_scaled`
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* Support use of `alpha` and `beta` in `tex.generic_gemm`
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* Remove calls to `validate_gemm_scale` from `BasicLinear`
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* Unfused impl for dbias-quantize
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* Unfused impl for dact-dbias-quantize
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* Disable fused bgrad-quantize for unsupported recipes
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* Remove unfused dbias-quantize impls

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* Support unfused impls in tex functions
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* Tweaks
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* Remove unused imports
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* fix: Add stream synchronization before destroying MPI communicator (#1979)
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* feat: Implement column-wise userbuffer overlap for comm+GEMM operations

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* fix: Working userbuffer overlapping API
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[PyTorch] Fix numeric overflow caused by int-type parameters and return value in the roundup function (#2034)
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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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* fix empty tensor list
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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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* 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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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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* Initialize metatensor values
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* Fuse computation of amax into the activation kernel for fp8 current scaling
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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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* Fix linter warnings
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* fix input_quantizer usage in Linear bwd
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* Review suggestions
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* functional passed
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* [PyTorch|common] Implement unpadding kernel for FP8

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