Commits · 830ef60fd89508ee7c372bcc1489c0beb86094cb · OpenDAS / TransformerEngine

02 Jan, 2026 1 commit
- Update copyright to include year 2026 (#2553) · 830ef60f
  Kirthi Shankar Sivamani authored Jan 02, 2026
```
Update copyright to include 2026
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
```
  830ef60f
20 Dec, 2025 1 commit

[PyTorch][NVFP4][MOE] NVFP4 Grouped Quantize with Hadamard Transform (#2411) · eb8e792b

Zhongbo Zhu authored Dec 20, 2025



* rowwise colwise RHT group quant v1
Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>

* remove local array RW
Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>

* change wait_barrier
Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>

* fast math options
Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>

* use mult to replace div
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* format
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* bulk move random states
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* greptile
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* lint
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* revert to use divides
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* avoid fp32 bf16 round-trip in RHT cast fusion
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* trigger fastmath by toggle NVTE_RHT_CAST_FUSION_USE_FAST_MATH
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* integrate row col rht fusion, functional
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* numerics aligned
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* style
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* remove device sync
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* 128 padding
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* revert colwise rng state creation because of row-col fused kernel
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* fix CI, linter
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* refactor RS for generating two random values
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* Avoid invalid configs with templated kernel
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* fix acc pipeline init with 0 arrival count
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* restore rowwise-only mode
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* switch to dynamic atomic scheduler
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* Avoid instantiating group RHT+cast kernel without row-wise or col-wise output
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* Include fast math option in quantization config
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* Fix linter warnings and review nits
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* Use TE license
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* Restore BF16 intermediate downcast in fused RHT-cast kernels
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* fix numerical test of grouped kernel
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* Make sure row-wise and col-wise quantization use different RNG seeds
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---------
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Signed-off-by: Tim Moon <tmoon@nvidia.com>
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eb8e792b

25 Nov, 2025 1 commit

[PyTorch][NVFP4][MOE] NVFP4 Grouped Hadamard Amax Kernel (#2351) · 89cc2a7e

Zhongbo Zhu authored Nov 25, 2025



* minor fix of torch view dtype
Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>

* multi-tensor RHT amax, compiles
Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>

* setup multi_tensor_quantize_nvfp4_impl
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* wire things up and run without crash
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* numerical test
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* unit test passing
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* finish unit test of split quantize api
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* bump up padding to 64 for nvfp4 grouped quantize
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* fix stochastic rounding
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* lint
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* change error message
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* clean up
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* enable multi-amax without RHT
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* fix col-only quantize mode
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* improve benchmark script
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* add NCU example script
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* add larger test case
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* add contiguous_data_and_scale check to bulk allocator
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* unified naming and differentiate between group_ and multi_
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* move regular amax into multi_tensor.h
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* Disentangle logic for split-quantize and general multi-tensor quantize
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* Use size_t for split sections
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* Suggestions from @greptile-apps
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Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>
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89cc2a7e

21 Oct, 2025 1 commit

[PyTorch][MOE] Support NVFP4 Grouped Linear (#2215) · b4a1d4d6

Zhongbo Zhu authored Oct 20, 2025



* pipeclean, fix nvfp4 padding of 32 alignment
Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>

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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

Tensor shape logic still requires treating 0-D tensor as uninitialized.
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* Fix invalid conversion from tensor to int
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Signed-off-by: Zhongbo Zhu <zhongboz@nvidia.com>
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b4a1d4d6

14 Oct, 2025 1 commit

Generalize quantization APIs for FP8/FP4/.. recipes (#2256) · 85a91997

Kirthi Shankar Sivamani authored Oct 14, 2025



* Initial API change
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Change all imports and api
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* format
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* fixes
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* fix typo
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* fix recipe tets
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* fix more tests
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* fix docs, tests, and make Jax change as well
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* Change internal uses of fp8_autocast
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* Address nits
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* rename file
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* CG function, and small test fixes
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* Change instances of make_graphed_callables internally
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* Fix distributed tests
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* Review
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* Review
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* Fix test and add more docs
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* Cleanup test imports and minimize internal file imports
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* Make is_bf16_available public
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* fixes
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* fix tests
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* Better docs and better api
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* format
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* Apply suggestions from code review
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* fix nvfp4 test
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85a91997

06 Aug, 2025 1 commit

[PyTorch] Multi-tensor swizzle scaling factors for MXFP8 and fuse padding zeros (#2019) · c0d2f1a5

Xin Yao authored Aug 07, 2025



* for loop
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* bulk alloc
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* multi-tensor swizzle
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* pad zeros in swizzle kernels
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* unify single- and multi-tensor swizzle
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* fix empty tensor list
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* fix bug for col swizzle
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* check context & fix signifiers
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c0d2f1a5

09 Jul, 2025 1 commit

[PyTorch][MoE] MXFP8 Support to Reduce CPU Overhead By Fuse Torch Empty Calls (#1934) · 96ee7173

Zhongbo Zhu authored Jul 09, 2025



* functional passed
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* before zero padding in mxfp8 swizzle, use torch zeros to malloc for now
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* format
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* lint
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---------
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96ee7173

26 Jun, 2025 1 commit

[PyTorch][MoE] Reduce CPU Overhead By Fuse Torch Empty Calls (#1793) · c30e961f

Zhongbo Zhu authored Jun 25, 2025



* finish python ref impl for bulk alloc
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* c++ bulk alloc worked, still draft version
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* clean up
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* resolve rebase conflict
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* add license
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* use shared_ptr to auto manage reference count
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* attempt to fix misc training error
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* attempt to handle case where experts get zero token
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* updated with fused C++ function calls
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* clean up
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* experiment with reducing py object construction time
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* fix seg fault bug in inference mode
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* fix lint
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* fuse torch split into bulk alloc
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* rebase to latest main
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* refactor quantize to call quantize_cpp
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c30e961f