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* Add NVFP4 recipe
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Co-authored-by: Frank Sun <frsun@nvidia.com>
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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"

This reverts commit f8b2a2d0111d9af690b43bb98ae448d9a430a185.
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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

Use logical tensor dims for PyTorch NVFP4 tensors. Temporarily add unfused dequantize impl. Fix bug where NVFP4 recipe was not configurable.
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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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* clean up duplicated code in nvfp4_utils.cuh
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* lint
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* pylint: disable=unused-argument
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* `nvte_cublas_gemm_v2` to take alpha pointer (#12)

* make nvte_cublas_gemm_v2 to take alpha/beta pointers
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* users are expected to pass a valid C_tensor
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* typos
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* API to have const float* alpha
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* Minor tweaks

Support arbitrary beta scales. Increase workspace to be aligned to 128 bytes.
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* Debug IMA with alpha pointer
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* Support fused amax kernels with NVFP4 quantization
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* Disable fused amax with cuDNN LayerNorm kernel
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* Add NVFP4 cases to distributed tests for TE ops
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* Change assert to NVTE_CHECK in the hadamard cast fusion
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* Fix compile error
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* Use global thread IDs for Philox subsequences
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* Add shape checks for NVFP4 cast kernels
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* Do not fuse amax if cuDNN normalization is forced by envvar
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* fix cross entropy
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* fix comments
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* fix: few more style issues
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* fix: remove grad_output_stride (unnecessary)
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* fix: only backward was broken
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* Generalize cross entropy backward kernel to handle reduced and unreduced loss
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* Remove if-else and torch.tensor to meet cudagraph requirement
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* Add is_cg_capturable flag to guard the if-else statement
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* optimize permute
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* fix lint
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* Fix an issue when mcore uses te fusion ce implementation
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* simplify unit test code
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* Update tests/pytorch/test_parallel_cross_entropy.py
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* Added token ignoring for CE loss
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* Added tests
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* add support for new recipe on permute_fusion, rm fp unpermute
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* fix lint
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* remove fp8 from index map
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* skip unsupported tests
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fix cpu device import error
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* Added parallel cross entropy loss implementation using online softmax
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* Added tests
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* Added reshape of loss output
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* Added to test list
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* Added Triton dependency
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* Fixed lint errors
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* Update setup.py
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* Added reason arg
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* Do not register Triton dependency with setuptools
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* add prob permute; fix fp8tensor
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* revert unnecessary changes in UT
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* remove unnecessary probs dtype convert
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* refine the doc string
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* fix lint
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* use fp32 compute type
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* style fix
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* fix empty input return
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* add mask-based moe permutation

* change moe_chunk_permute to moe_sort_chunks_by_indices

* fix __all__ in pytorch/permutation.py

* fix func/var names and typos; update tols in UT

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