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cch/deepgemm-glm52-nvfp4-group16
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120 Commits

Author SHA1 Message Date
LuminolT
007c645f87 fix(megamoe): normalize fp4 weight preparation contract 
Separate source and destination FP4 scale packing in requant_fp4_to_gran_k so group16-to-group32 conversion always recomputes UE8M0 runtime scales by default.

Make prepare_fp4_weights_for_mega_moe accept raw grouped FP4 weights and scales, then perform optional requantization, DeepGEMM scale layout transform, and MegaMoE UTCCP weight transform internally.

Update the MegaMoE synthetic benchmark so baseline grouped GEMM uses runtime-layout weights while fused MegaMoE uses transformed weights from the same raw source tensors.

Tested: PYTHONPYCACHEPREFIX=/private/tmp/deepgemm_pycache python3 -m py_compile deep_gemm/__init__.py deep_gemm/mega/__init__.py deep_gemm/utils/math.py tests/test_layout.py tests/test_mega_moe.py

Tested: git diff --check

Not-tested: CUDA build, SM100/B300 runtime, and GLM-5.2 accuracy validation are not available locally.
 2026-07-08 18:48:04 +08:00
LuminolT
2c7543130b feat(megamoe): expose fp4 weight preparation helper 
Add a top-level MegaMoE helper that handles source/runtime FP4 weight granularity before applying the existing MegaMoE weight layout transform.

Use the helper from the synthetic MegaMoE benchmark so SGLang can later follow the same contract for GLM-5.2 NVFP4 group16 checkpoints.

Tested: PYTHONPYCACHEPREFIX=/private/tmp/deepgemm_pycache python3 -m py_compile deep_gemm/__init__.py deep_gemm/mega/__init__.py tests/test_mega_moe.py

Tested: git diff --check

Not-tested: CUDA build, SM100/B300 runtime, and GLM-5.2 accuracy validation are not available locally.
 2026-07-08 18:36:40 +08:00
LuminolT
8ad348fb11 feat(megamoe): add fp4 group16 to group32 requant path 
Add a utility and synthetic benchmark path for converting FP4 group16 tensors into group32 tensors that the existing SM100 block32 MegaMoE kernels can consume.

Document that this is a requantization path rather than a lossless metadata rewrite, so GLM-5.2 accuracy validation is still required before production use.

Tested: PYTHONPYCACHEPREFIX=/private/tmp/deepgemm_pycache python3 -m py_compile deep_gemm/utils/math.py tests/test_layout.py tests/test_mega_moe.py

Tested: git diff --check

Not-tested: CUDA build, SM100/B300 runtime, and GLM-5.2 accuracy validation are not available locally.
 2026-07-08 18:34:33 +08:00
LuminolT
79fcfd6abf feat(megamoe): add nvfp4 group16 capability gate 
Allow SM100 FP4 scale layout transforms to accept group16 and thread weight granularity through the MegaMoE Python wrapper, API checks, and synthetic benchmark entrypoint.

Keep fused SM100 MegaMoE compute behind an explicit group16 capability gate until the SFB/TMEM/MMA scale path is updated and validated.

Tested: PYTHONPYCACHEPREFIX=/private/tmp/deepgemm_pycache python3 -m py_compile deep_gemm/mega/__init__.py tests/test_mega_moe.py tests/generators.py

Tested: git diff --check

Not-tested: CUDA build and SM100/B300 runtime validation are not available locally.
 2026-07-08 18:29:09 +08:00
Xinyi Liu
453fc7b046 feat: implement sm90 megamoe phase6 combine 2026-06-18 17:40:49 +08:00
Xinyi Liu
71f012a356 style: fix sm90 megamoe dispatch indentation 2026-06-18 15:54:00 +08:00
Xinyi Liu
9bd0519605 feat: implement sm90 megamoe phase5 l2 scatter 2026-06-18 15:17:20 +08:00
Xinyi Liu
f3553f976c feat: implement sm90 megamoe phase4 l1 epilogue 2026-06-18 01:09:45 +08:00
Xinyi Liu
842124b1c5 feat: implement sm90 megamoe phase3 l1 wgmma 2026-06-18 00:46:41 +08:00
Xinyi Liu
540e5aeadc feat: implement sm90 megamoe phase2 dispatch-only 2026-06-18 00:00:36 +08:00
Xinyi Liu
1dafd0f173 feat: add sm90 megamoe phase1 interfaces 2026-06-17 23:54:49 +08:00
Zhean Xu
891d57b4db Add various optimizations and Mega MoE benchmarks (#316) 
* Merge with private repo

* Add Mega MoE Benchmark

* Minor fix

* Update

---------

Co-authored-by: Chenggang Zhao <chenggangz@deepseek.com>
 2026-04-24 18:41:37 +08:00
Chenggang Zhao
7f2a703ed5 [Public release 26/04] Introducing Mega MoE, FP4 Indexer and other features/fixes (#304) 
* Merge with private repo

* Update README

* Update README

* Update README

* Add PyTorch requirements

* Fix sync scopes for MQA logits (#256)

* Update README
 2026-04-17 09:45:14 +08:00
Ray Wang
d30fc36c8f Fix sync issue of TMEM alloc/dealloc (#292) 2026-03-22 16:41:28 +08:00
Xin Qiu
35c4bc8771 fix: k_grouped_fp8_gemm_nt_contiguous crashes with n = 768 on H100 (#238) 2026-02-25 10:13:54 +08:00
Ray Wang
477618cd51 Fix a sync issue in SM100 MQA logits (#285) 2026-02-03 17:29:49 +08:00
Zhean Xu
0f5f266202 Multiple updates and refactorings (#280) 2026-01-16 17:06:52 +08:00
Ray Wang
38f8ef73a4 Multiple updates and refactorings (#231) 2025-11-21 17:49:47 +08:00
Zhean Xu
bb4424aad4 Fix sum_k * shape_m overflow 2025-11-19 11:51:36 +08:00
Ray Wang
ec5e9ed0b8 Fix SM90 MQA logits (#229) 2025-11-19 10:50:36 +08:00
Ray Wang
2f9d87877e Use larger MMA shape (#227) 2025-11-14 11:38:15 +08:00
oliver könig
9f196058ae chore: Build and store bdist wheels (#181) 
* build: Minor tweeks for wheel build

Signed-off-by: oliver könig <okoenig@nvidia.com>

* ci: Workflows for wheel build

Signed-off-by: oliver könig <okoenig@nvidia.com>

* fix

Signed-off-by: oliver könig <okoenig@nvidia.com>

* fix

Signed-off-by: oliver könig <okoenig@nvidia.com>

* build: Add CachedWheel

Signed-off-by: oliver könig <okoenig@nvidia.com>

* add version to init

Signed-off-by: oliver könig <okoenig@nvidia.com>

* revert

Signed-off-by: oliver könig <okoenig@nvidia.com>

* revert

Signed-off-by: oliver könig <okoenig@nvidia.com>

* revert

Signed-off-by: oliver könig <okoenig@nvidia.com>

* v2

Signed-off-by: oliver könig <okoenig@nvidia.com>

* update

Signed-off-by: oliver könig <okoenig@nvidia.com>

* test

Signed-off-by: oliver könig <okoenig@nvidia.com>

* from packaging.version import parse

Signed-off-by: oliver könig <okoenig@nvidia.com>

* local version

Signed-off-by: oliver könig <okoenig@nvidia.com>

* remove file

Signed-off-by: oliver könig <okoenig@nvidia.com>

* revert

Signed-off-by: oliver könig <okoenig@nvidia.com>

* Updates and lint

* revert missing cudaextension args

Signed-off-by: oliver könig <okoenig@nvidia.com>

* Add timeout

* fix DG settings

Signed-off-by: oliver könig <okoenig@nvidia.com>

* DG_USE_LOCAL_VERSION

Signed-off-by: oliver könig <okoenig@nvidia.com>

* Update version

* Detect local changes

* Minor fix

* Revert CUTLASS

* Unify options

---------

Signed-off-by: oliver könig <okoenig@nvidia.com>
Co-authored-by: Chenggang Zhao <chenggangz@deepseek.com>
 2025-10-10 18:23:40 +08:00
Chenggang Zhao
c1bf4cae4b Fix version 2025-10-01 20:31:27 +08:00
Chenggang Zhao
07b82fb8cd Fix old CUDA compatibility 2025-10-01 20:29:15 +08:00
Simon Mo
59f2c07cf2 Add SM100 kernels (#201) 
Signed-off-by: simon-mo <simon.mo@hey.com>
 2025-09-29 17:07:28 +08:00
Chenggang Zhao
80ceeb2c76 Add SM90 kernels (#200) 2025-09-29 17:00:23 +08:00
Ray Wang
3f71de7aa9 Make various updates and fixes (#198) 2025-09-25 16:19:07 +08:00
zhonghui-J
2da871e304 Fix grouped gemms performance issue. (#168) 2025-08-22 17:35:43 +08:00
Chenggang Zhao
e38c2e3103 Remove comments 2025-08-22 17:32:04 +08:00
Chenggang Zhao
f20256fd50 Compatible with CUDA 13 2025-08-22 17:30:47 +08:00
xiweny
affdb1cd90 Add sm_100f support and make nvcc 13 happy (#157) 
Signed-off-by: Xiwen Yu <13230610+VALLIS-NERIA@users.noreply.github.com>
 2025-08-22 17:19:32 +08:00
Ray Wang
f85ec649d7 Make various updates and fixes: (#164) 
- Add BF16 support for SM90 and SM100
- Refactor Python APIs
- Other fixes and code refactoring
 2025-08-15 18:32:35 +08:00
Ray Wang
d9c363f86f Make various updates and fixes: 
- Add support for legacy CUDA versions; now compatible with CUDA 12.3 and newer
- Add support for NVRTC compilation
- Other fixes and code refactoring
 2025-08-02 19:52:22 -07:00
yukuai26
aff9da0aba Fix SM90 GEMM (#149) 
* Fix sm90 GEMM

* Fix typo

---------

Co-authored-by: Kuai Yu <yukuai@deepseek.com>
 2025-08-01 10:36:49 +08:00
Ray Wang
9da4a23561 Add more GPU architectures support (#112) 
* Add more GPU architectures support

* Update layout.py

* Optimize performance, Add SM90 support, Add 1D2D SM100 support

* Add fmtlib submodule at commit 553ec11

---------

Co-authored-by: fzyzcjy <5236035+fzyzcjy@users.noreply.github.com>
 2025-07-18 11:32:22 +08:00
Chenggang Zhao
03d0be3d2d Simplify expression 2025-07-02 14:07:05 +08:00
fy1214
3fc6728dee [add] fix smem_barrier size in wgrad way (#122) 2025-07-02 14:05:36 +08:00
yukuai
e82c4139da Revert "Fixed the bug in get_swizzle_mode function related to elem_size setting. (#115)" 
This reverts commit ac428e25e0.

This PR causes wgrad to hang during testing. Revert it until we resolve the issue
 2025-06-23 17:13:36 +08:00
TherLF
ac428e25e0 Fixed the bug in get_swizzle_mode function related to elem_size setting. (#115) 2025-06-23 09:37:10 +08:00
shixianc
0c88cd0139 Fix illegal memory address when skipping -1 m indices (#113) 
Co-authored-by: Shixian Cui <shixian@amazon.com>
 2025-06-16 10:44:31 +08:00
yukuai26
8dfa329827 Grouped GEMM skip useless computation for unaligned Ms (#103) 
* Grouped GEMM skip useless computation for unaligned Ms

* Update readme.md

* small typo

* Rename variables

* Restore previous indent

* Format

* Refactor tests

* Add `SkipComputation` types

* Bug fixed

* Format

* Fix tests

* Add assertions

* Minor fix

---------

Co-authored-by: yukuai <yukuai@deepseek.com>
Co-authored-by: Chenggang Zhao <chenggangz@deepseek.com>
 2025-05-27 13:43:38 +08:00
Chenggang Zhao
391755ada0 Fix JIT tests 2025-05-16 14:39:58 +08:00
Chenggang Zhao
78d8362e7a Add a missing #pragma once 2025-05-15 18:10:05 +08:00
Chenggang Zhao
104a6ec109 Add __assertfail 2025-05-15 17:04:21 +08:00
Chenggang Zhao
3b412f458a Unify kwargs usages 2025-05-15 16:53:52 +08:00
Chenggang Zhao
350989eef3 Unify ceil_divs 2025-05-15 16:48:32 +08:00
Chenggang Zhao
4373af2e82 Add DG_PRINT_CONFIGS 2025-05-15 16:36:40 +08:00
Chenggang Zhao
816b39053a Refactor launch-related structures 2025-05-15 16:14:21 +08:00
Chenggang Zhao
e2d6a107ef Cleanup some useless staffs 2025-05-14 15:46:45 +08:00
Zhean Xu
04278f6dee Weight gradient kernels for dense and MoE models (#95) 
* Init weight gradient kernels.

* Support unaligned n,k and gmem stride

* Update docs

* Several cleanups

* Remove restrictions on N

* Add stride(0) assertions

---------

Co-authored-by: Chenggang Zhao <chenggangz@deepseek.com>
 2025-05-14 14:47:58 +08:00