GitLab

* Add general C API for setting tensor params
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* Implement general accessors for NVTETensor
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* Refactor tex swizzling to skip if scales are already swizzled
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* Support pre-swizzled scales in MXFP8Tensor
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* Add tex function to swizzle MXFP8 scales
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* Fix bug in inplace swizzle function
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* Tweak comments to use "compact/swizzled format"
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* MXFP8 quantize kernel with pre-swizzled scales
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* Expose pre-swizzled scales in modules
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* Support MXFP8 gated activations with swizzled scales
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* Add PyTorch infrastructure for pre-swizzled NVFP4 tensors
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* Deprecate DSv3-specific quantization logic in C API
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* Remove support for DSv3 compact data from quantizer
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* Remove DSv3 compact data format from core lib
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* Fix bug in FP8 all-gather
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* Fix linter warnings
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* Update JAX to use new swizzled scale API
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* Review suggestion from @greptile-apps
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* Update C++ swizzle test with swizzled scales API
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* Return default tensor params when querying params for invalid NVTETensor
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* Debug DSv3 FP8 test failures
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* Debug Userbuffers test failures
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* Make sure gated activations populate FP8 transpose if needed
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* Review suggestions from @greptile-apps
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* Disable pre-swizzling with debug quantizer
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* Fix merge conflicts and review suggestions

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* Use explicitly sized types in config accessors

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* Make util header for function that compute swizzled scale index
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* Update expected error message in FP8 block-scaling test
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Update copyright to include 2026
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* Initialize empty tensors with shape=[0] instead of shape=[].
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* Fix runtime crash in LayerNorm

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* Make sure norm workspace sizes are not zero
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* Remove unnecessary explicit call to default constructor
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* Avoid accessing tensor data pointer if tensor has no entries
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* Prefer using row-wise/col-wise shape based on which has data
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* Fix merge conflict, expand docs, fix inconsistency in dim function
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* Change Tensor::has_data to check whether tensor is initialized, not whether pointer is valid.
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* Review suggestion from @greptile-apps
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* Debug incorrect tensor initialization in tests
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* Clarify comments that has_data does not guarantee safe pointer accesses
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* Debug test failure when computing amaxes
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* pipeclean, fix nvfp4 padding of 32 alignment
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* numerical test passed
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* fix CI failure with test_cast_master_weights_to_fp8 (in a hacky way)
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* found CUDA mis-aligned address error in training in multi-swizzle, hack the vec_load_size to 1 to unblock
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* leave comments about alignment issue
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* fused bulk alloc nvfp4
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* fix RHT sign mask CPU overhead
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* fix
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* resolve comments
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* Remove incorrect logic that treats 0-D tensor as uninitialized

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* Fix invalid conversion from tensor to int
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* Add MathDx dependency to GitHub builds
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* Suggestions from GitHub Copilot
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* Move 2x shape logic from core to PyTorch
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* Fix compilation errors with CUDA 12.1
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* SM 70 is not supported in CUDA 13
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* Typo
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* Revert "Move 2x shape logic from core to PyTorch"

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* Added dequantize kernel for FP4
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* Fix linter warning
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* Add NVFP4 support with fusible ops

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* Fix logic for 2x shapes and move to PyTorch
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* Fix CG test model config
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* Debug NVFP4 tensor size function
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* Proper handling of the RNG state
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* Test SR properly
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* Fix workspace size for GEMM heuristic.
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* Fix compile error in C++ NVFP4 test

Some some numeric errors when blocks are all zero.
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* fix distrbuted test problem shape
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* proper assert dim for low precision AG TP
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* `nvte_cublas_gemm_v2` to take alpha pointer (#12)

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* API to have const float* alpha
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* Minor tweaks

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* Do not fuse amax if cuDNN normalization is forced by envvar
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