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6d7231a50e64c98bccdca9f5fdfd1c3214350a6e
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121 Commits

Author SHA1 Message Date
biondizzle
6d7231a50e fix: reinterpret float32 bits as uint32 before >> 23 for UE8M0 2026-05-11 09:42:03 +00:00
biondizzle
03b8c99ee1 fix: use mxf8f6f4 (UE8M0) on SM100 — mxf4nvf4 requires SM103+ 
B200 (SM100) does NOT support kind::mxf4nvf4 at all (neither 2X nor 4X).
Only mxf8f6f4.block_scale with UE8M0 scales is available on SM100.

Strategy: keep NVFP4 E2M1 weights, convert UE4M3 block scales → UE8M0
in the weight transformation. This is a scale format adaptation for
hardware compatibility, not a format conversion.

Changes:
- Kernel: back to mxf8f6F4 instruction + float_ue8m0_t descriptor
- L1 epilogue: back to UE8M0 (>> 23) activation scales
- Python: merge block16→block32, convert UE4M3→float32→UE8M0
- Packing: uint8 (UE8M0) → int32, same as MXFP4
 2026-05-11 09:28:45 +00:00
biondizzle
cd7a612175 debug: add shape logging to SF packing 2026-05-11 08:54:14 +00:00
biondizzle
dcebe033e2 fix: use scale_vec::2X (block32) for SM100 B200 compatibility 
scale_vec::4X (block16) requires SM103/SM120 (B300/GB300), not SM100 (B200).
Revert to block32 with UE4M3 scales. Same TMEM layout as MXFP4 but with
UE4M3 scale format instead of UE8M0.

Changes:
- kGranK: 16 → 32
- PTX: scale_vec::4X → scale_vec::2X
- SF layout: same as MXFP4 (K/32, K/128 for int32 packed)
- UTCCP: i*8 → i*4 (2X layout, same as MXFP4)
- TMEM columns: same as MXFP4 (SF_BLOCK_M/32, SF_BLOCK_N/32)
- Python: merge NVFP4 block16→block32 scales (max of adjacent pairs)
- recipe: (1,1,16) → (1,1,32)
 2026-05-11 08:36:59 +00:00
biondizzle
deff80c9c1 fix: add Python wrapper for NVFP4 SymmBuffer allocation 
get_symm_buffer_for_nvfp4_mega_moe uses _C.get_symm_buffer_size_for_nvfp4_mega_moe
to allocate the correct buffer size (2x SF entries due to group_size=16).
Custom init to avoid SymmBuffer's hardcoded MXFP4 allocation.
 2026-05-11 08:05:21 +00:00
biondizzle
8d02eb38fa fix: transpose SF to MN-major layout before TMA stride checks 
transform_sf_into_required_layout expects MN-major input (stride(-2)=1).
Our packed int32 SF is K-major (stride(-1)=1). Transpose the last two
dims, make contiguous, then transpose back so data is in MN-major order.
 2026-05-11 07:32:10 +00:00
biondizzle
7154500f22 fix: reshape SF to 2D before transform_sf_into_required_layout 
The C++ check_sf_layout stride assertion fails on 3D (experts, mn, K//64)
tensors. Reshape to 2D (experts*mn, K//64) before calling the transform
function, matching the expected stride layout.
 2026-05-11 07:30:54 +00:00
biondizzle
388fd8dcfd fix: pack UE4M3 into int32 before transform_sf_into_required_layout 
The C++ transform function expects int32 (for kInt type) with 4 UE4M3
bytes packed per int32. We pack first, then transform for TMA alignment
and UTCCP transpose with recipe (1, 16).
 2026-05-11 07:05:11 +00:00
biondizzle
acae75e109 fix: use transform_sf_into_required_layout for proper TMA-aligned SF 
Instead of custom _pack_nvfp4_sf_for_utccp, use DeepGEMM's C++
transform_sf_into_required_layout with recipe (1, 1, 16) for NVFP4.
This handles TMA alignment and UTCCP transpose correctly.
 2026-05-11 06:54:34 +00:00
biondizzle
5cb4fcaef3 fix: cast uint8 weights to int8 (kPackedFP4) for DeepGEMM compatibility 2026-05-11 06:36:32 +00:00
biondizzle
bbf9a5f46a feat: fold weight_scale_2 into block scales in NVFP4 transform 
- transform_nvfp4_weights_for_mega_moe now accepts weight_scale_2
- Folds global scale into block scales: UE4M3 * FP32 -> UE4M3
- Dequantize to f32, multiply by global scale, clamp [0,448], re-quantize
- This is needed because the kernel only applies one level of block scaling
 2026-05-11 05:42:16 +00:00
biondizzle
36b439ee26 feat: NVFP4 mega MoE kernel (scale_vec::4X, UE4M3 block scales) 
- New CUDA kernel: sm100_fp8_nvfp4_mega_moe_impl
  - kGranK=16 (NVFP4 group_size=16, vs MXFP4's 32)
  - kind::mxf4nvf4.block_scale.scale_vec::4X PTX instruction
  - float_ue4m3_t scale factor type in instruction descriptor
  - SF layout: scale_vec::4X (4 TMEM sub-columns per UMMA atom)
  - UTCCP column stride: i*8 (vs MXFP4's i*4) for 4X layout
  - L1 epilogue: UE4M3 activation scales (float→cutlass::float_e4m3_t)
  - SF loading: kNumSFUint32 = kHidden/64 (4 UE4M3 per int32)

- New PTX wrappers: SM100_MMA_MXF4NVF4_2x1SM_SS, SM100_MMA_MXF4NVF4_SS

- Python API:
  - fp8_nvfp4_mega_moe() with recipe=(1,1,16)
  - transform_nvfp4_weights_for_mega_moe() for UE4M3→int32 UTCCP packing
  - _pack_nvfp4_sf_for_utccp() helper

- C++ bindings:
  - mega_nvfp4.hpp with NVFP4-specific SymmBuffer (SF stride K/16)
  - JIT kernel header with kGranK=16 TMA descriptors
  - Registered in python_api.cpp

NOTE: Both SFA and SFB must use UE4M3 (scale_format_ is 1-bit, shared).
The L1 epilogue converts float→UE4M3 for activation scales.
 2026-05-11 05:41:08 +00:00
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