Commit Graph
54 Commits
Author SHA1 Message Date
Andreas KieslingerandOliver Simons 9e58d4d692 Avoid PDL race conditions by disabling __restrict__ when PDL is used (#24030)
* Removes __restrict__ from PDL kernel headers due to incompatibility with
PDL. Adds preprocessor directives based on arch in kernel body to add
__restrict__ to retain performance on older architectures.

* Simplifies new __restrict__ usage via macro

* Add hopper to PDL __restrict__ fix.

Co-authored-by: Oliver Simons <osimons@nvidia.com>

---------

Co-authored-by: Oliver Simons <osimons@nvidia.com>
2026-06-03 13:56:42 +02:00
Aman GuptaandJohannes Gäßler f8f0a47a55 cuda: reserve space for quantize kv-cache at startup (#23907)
* cuda: reserve space for quantize kv-cache at startup

* address review comments

* remove forward decl

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* remove assert in ggml-cuda.cu

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2026-06-03 18:39:59 +08:00
Andreas Kieslinger 241cbd41d2 cuda : disables launch_fattn PDL enrollment due to compiler bug (#23825) 2026-05-29 07:46:10 +03:00
Andreas Kieslinger e947228222 Programmatic Dependent Launch (PDL) for more performance on newer NVIDIA GPUs (Hopper+) (#22522)
* Adds initial PDL setup.

* Adds PDL barriers based on simple heuristic: place "sync" before first input pointer access, and "launch" after last write, e.g. to tensors like dst.

* Further optimization pass of the first half of kernels

* Optimized PDL barriers for the second batch of kernels

* Further refinements after rebase.

* Moves pdl logic to separate function, removes some whitespace

* Strips post-hoc PDL logic

* Adds stream capture PDL setup. Enrolls quantize_q8_1 to leverage pdl to
overlap execution with previous kernels

* Enrolls mul_mat_vec_q, rms_norm_f32 and k_bin_bcast (partly) into PDL

* Enrolls mmvf, rope, set-rows and topk kernels for gpt-oss into PDL

* Introduce ggml_cuda_kernel_launch, to abstract away cudaLaunchKernelEx,
to enable hip/musa compatibility

* Enrolls cpy_scalar_contiguous, k_get_rows_float and rms_norm_f32

* Enrolls flash_attn_combine_results

* Fix: Drops needless and broken check of CUDA arch for PDL. PDL either
works or is without effect.

* Enrolls flash-attention kernels to pdl

* Fix: inlines ggml_cuda_kernel_launch, and uses perfect forwarding for
kernels args. This fixes PDL.

* Perf: Enrolls k_bin_bcast variadic template invocation into PDL, via
and template alias and template expansion

* Enrolls all remaining kernels for qwen3-coder-next into PDL

* Remove all PDL LC calls to create a baseline

* Added LC according to internal guidance and tested kernel performance.

* Enrols missing qwen3-5 kernels passively into PDL.

* Kernel optimizations (LC signals) for qwen3.5

* Enrolls ssm-scan kernels into PDL

* Adds GGML_CUDA_PDL command line option to toggle PDL.

* Fix: Ada and lower compilation by guarding PDL calls correctly

* Cleanup: Removes commented out GGML_CUDA_PDL_LC

* Cleanup: Removes experimental comments

* Adds 90-virtual to build script so that Hopper GPUs can leverage PDL.

* Adds stricter checks to enable PDL, adds env-check to disable it, and removes now superfluous compile option to enable PDL.

* Fix: Correct PDL en/disablement based on device-side arch check. Host
side check is UB. Required moving from macros to inlined functions

* Fix: default-disable PDL. Enable by setting GGML_CUDA_ENABLE_PDL=1

* Enable PDL by default for Hopper+ devices

* Enrolls softcap_f32 and two flash_attn kernels into PDL.

* Improves flash attn PDL barrier placement

* Fix: Perf regression on ada; excludes ada and below from PDL launches

* Improves some sync barrier placements

* Drops superfluous constructor

* Adds #endif guard comments

* Reverts experimental change to top-k-moe.cu, which moved expensive allocations
in front of the PDL barrier. It did not have a meaningful impact.

* Exchanges GGML_CUDA_DISABLE_PDL with GGML_CUDA_PDL. IFF GGML_CUDA_PDL=0
PDL is disabled

* Revert "Drops superfluous constructor". Adds const to remaining
arguments

This reverts commit 12b1d250da0089ae02a9bb71bbb3fd6d70f6f2f1.

* Cleanup: Removes and fixes some comments and whitespace

* Clarifies comment of sync-barrier position

* Relocates and refactors PDL launch functions and accessories

* Adds error checking to the regular kernel launch path

* Drops "auto" in favor of "ggml_cuda_kernel_params"

* Adds "const" to ggml_cuda_kernel_launch_params

* [Whitespace] Adds final newline to common.cuh to make editorconfig CI job happy
2026-05-20 13:59:02 +02:00
Gaurav Garg 15f786e658 [CUDA ] Write an optimized flash_attn_stream_k_fixup kernel (#21159)
* Write an optimized flash_attn_stream_k_fixup kernel

Write a specialized and more optimized kernel for cases where nblocks_stream_k is multiple of ntiles_dst.
Make nblocks_stream_k to multiple of ntiles_dst if nblocks_stream_k > 2 * ntiles_dst

* Use the new kernel only for nblocks_stream_k_raw > 4 * ntiles_dst to make sure we have enough concurrency on GPUs

* Address review comments

* Address review comments

* Revert variable names to original
2026-04-06 20:34:29 +02:00
Patrick BuckleyandJohannes Gäßler db9d8aa428 ggml-cuda: native bf16 flash attention for vec kernel (#20525)
* ggml-cuda: native bf16 flash attention for vec and tile kernels

mma kernel still converts bf16 to fp16 before launch, native mma bf16 todo

* ggml-cuda: address code owner review feedback

reverted tile kernel changes to avoid larger refactor

* fix ci failures on turing and hip

* fix bf16 vec kernel compile on hip v_dot2 platforms

* add comments

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2026-03-22 11:05:51 +01:00
Johannes Gäßler ae40cd27c8 CUDA: limit number of FA stream-k CUDA blocks (#20586) 2026-03-15 18:30:47 +01:00
Georgi Gerganov 631cbfcc7a cuda : fix "V is K view" check for non-unified KV cache (#19145) 2026-01-28 09:15:27 +02:00
Johannes Gäßler b0311c16d2 CUDA: fix padding of GQA to power of 2 in FA (#19115) 2026-01-26 23:24:58 +01:00
Johannes Gäßler 0c21677e43 CUDA: faster FA for GQA > 1 but not power of 2 (#19092) 2026-01-25 21:19:47 +01:00
Georgi Gerganov d9c6ce46f7 kv-cache : support V-less cache (#19067)
* kv-cache : support V-less cache

* cuda : better check for V_is_K_view

* cuda : improve V_is_K_view check

* graph : add comments

* hparams : refactor
2026-01-25 15:48:56 +02:00
Johannes Gäßler 8f91ca54ec CUDA: re-use MLA K data for V in MMA FA (#19057) 2026-01-24 10:09:36 +01:00
Georgi GerganovandJohannes Gäßler a5eaa1d6a3 mla : make the V tensor a view of K (#18986)
* mla : pass V as a view of K to the FA op

* cuda : adjust mla logic to new layout

* kv-cache : fix rope shift

* tests : remove comment

* cuda : fix reusable_cutoff

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2026-01-22 22:09:01 +02:00
Johannes Gäßler 5c662d21a3 CUDA: fix allignment on register spill for FA (#18815) 2026-01-15 15:14:50 +01:00
ea4a321f2a HIP: add fattn-mma-f16 for RDNA4 (#18481)
* finish VQ mma

* flash_attn_ext_f16_iter

* KQ_rowsum

* correct exp

* fix scale error

* fix softmax scale

* fix softmax scale

* enable fattn on cpu side

* fix random error

* disable fattn-mma-f16 on rdna3

* fix wrong col for rdna

* use identity mat to transpose

* resolve conflicts

* basic tuning for DeepSeek-R1-Distill-Qwen-1.5B

* fix volta compile error

* align rdna4 policy for fattn

* adjust fattn policy

* adjust kernel selection logic

* update as the review comments

* keep fattn-wmma logic

* adjust kernel selection logic

---------

Co-authored-by: zhang hui <you@example.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2026-01-13 13:52:16 +01:00
Johannes Gäßler df17a4c94f CUDA: fix FA FP16 accumulator overflow for Granite (#18614) 2026-01-05 19:51:13 +01:00
Johannes Gäßler 0f2e42ca1d CUDA: only allocate FA tmp buffer if needed (#18564) 2026-01-03 13:55:53 +01:00
pl752andpl752 9dba9f5352 (Bugfix, ggml-cuda) Pool alloc count fix + small size computation type adjustment (#18559)
* CUDA: Fixed obj byte size instead of obj count being passed to pool alloc (fattn-common, dst_tmp_meta)

* CUDA: Explicitly casted some of the int alloc counts before multiplication in argsort

---------

Co-authored-by: pl752 <maximpl752@gmail.com>
2026-01-03 11:13:40 +01:00
Johannes Gäßler 482211438d CUDA: fix overflow in MMA kernel without stream-k (#17939) 2025-12-12 17:43:58 +01:00
Johannes Gäßler e95d0bc8fd CUDA: fix FA VKQ accumulator overflow (#17746) 2025-12-05 09:18:10 +01:00
Johannes GäßlerandAman Gupta <aman> 2e1c9cd814 CUDA: generalized (mma) FA, add Volta support (#17505)
* CUDA: generalized (mma) FA, add Volta support

* use struct for MMA FA kernel config

---------

Co-authored-by: Aman Gupta <aman>
2025-12-03 16:57:05 +01:00
Johannes Gäßler 73955f7d2a CUDA: no FP16 arithmetic for vector FA kernel (#17558) 2025-11-28 10:29:09 +01:00
Johannes Gäßler 51d1a8c997 CUDA: better error for FA kernel with 0 occupancy (#16643) 2025-10-21 15:27:53 +02:00
Johannes Gäßler 11f0af5504 CUDA: faster tile FA, add oob checks, more HSs (#16492) 2025-10-11 20:54:32 +02:00
Johannes Gäßler 75a3a6c2cd CUDA: refactor and deduplicate vector FA kernels (#16208)
* CUDA: refactor and deduplicate vector FA kernels
2025-09-27 18:45:07 +02:00
Johannes Gäßler c959b676be CUDA: fix FA occupancy, optimize tile kernel (#15982) 2025-09-17 15:32:42 +02:00
Johannes Gäßler 13aeb7aef2 CUDA: refactor FA support/selection code (#15454) 2025-08-20 23:14:14 +02:00
Johannes Gäßler 4227c9be42 CUDA: fix negative KV_max values in FA (#15321) 2025-08-14 23:21:24 +02:00
fd1234cb46 llama : add gpt-oss (#15091)
* oai moe

* compat with new checkpoint

* add attn sink impl

* add rope scaling yarn

* logits match with latest transformers code

* wip chat template

* rm trailing space

* use ggml_scale_bias

* rm redundant is_swa_all

* convert interleaved gate_up

* graph : fix activation function to match reference (#7)

* vocab : handle o200k_harmony special tokens

* ggml : add attention sinks support (#1)

* llama : add attn sinks

* ggml : add attn sinks

* cuda : add attn sinks

* vulkan : add support for sinks in softmax

remove unnecessary return

* ggml : add fused swiglu_oai op (#11)

* ggml : add fused swiglu_oai op

* Update ggml/src/ggml-cpu/ops.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* update CUDA impl

* cont : metal impl

* add vulkan impl

* test-backend-ops : more test cases, clean up

* llama : remove unfused impl

* remove extra lines

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>

* repack mxfp4 upon conversion

* clean up a bit

* enable thinking

* add quick hack to render only some special tokens

* fix bf16 conversion

* remove vocab hack

* webui ok

* support chat parsing for gpt-oss

* fix webui

* direct mapping mxfp4, FINALLY

* force using mxfp4

* properly use lazy tensor

* ggml : add mxfp4

ggml : use e8m0 conversion instead of powf

Co-authored-by: Diego Devesa <slarengh@gmail.com>

change kvalues_mxfp4 table to match e2m1 (#6)

metal : remove quantization for now (not used)

cuda : fix disabled CUDA graphs due to ffn moe bias

vulkan : add support for mxfp4

cont : add cm2 dequant

* ggml : add ggml_add_id (#13)

* ggml : add ggml_add_id

* add cuda impl

* llama : add weight support check for add_id

* perf opt

* add vulkan impl

* rename cuda files

* add metal impl

* allow in-place ggml_add_id

* llama : keep biases on CPU with --cpu-moe

* llama : fix compile error

ggml-ci

* cuda : add fallback for __nv_cvt_e8m0_to_bf16raw

ggml-ci

* cleanup

ggml-ci

* sycl : fix supports_op for MXFP4

ggml-ci

* fix Unknown reasoning format

* ggml-cpu : fix AVX build

ggml-ci

* fix hip build

ggml-ci

* cuda : add mxfp4 dequantization support for cuBLAS

ggml-ci

* ggml-cpu : fix mxfp4 fallback definitions for some architectures

ggml-ci

* cuda : fix version required for __nv_cvt_e8m0_to_bf16raw

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: slaren <slarengh@gmail.com>
2025-08-05 22:10:36 +03:00
Johannes Gäßler 92b8810ec7 CUDA: skip masked KV slices for all FA kernels (#14924) 2025-07-30 15:46:13 +02:00
uvos aa79524c51 HIP: remove the use of __HIP_PLATFORM_AMD__, explicitly support only AMD targets (#14945) 2025-07-29 20:23:04 +02:00
Johannes Gäßler a86f52b285 CUDA: fix overflow in FA, tune performance (#14840) 2025-07-23 21:43:25 +02:00
Johannes GäßlerandGeorgi Gerganov 07a19e27a2 CUDA: fix quantized KV cache + multiple sequences (#14822)
* CUDA: fix quantized KV cache + multiple sequences

* Update ggml/src/ggml-cuda/fattn-common.cuh

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-07-23 14:08:09 +03:00
Georgi GerganovandJohannes Gäßler 225e7a1438 llama : add high-throughput mode (#14363)
* kv-cache : prepare K/V buffers for separation

ggml-ci

* batched-bench : fix oob write

ggml-ci

* llama : add "virtual sequences"

ggml-ci

* llama : use "stream" vs "virtual sequence"

ggml-ci

* graph : fix stream splitting when KV cache is not used

ggml-ci

* kv-cache : add multi-stream save/load support

ggml-ci

* llama : add "--attn-streams" flag

ggml-ci

* kv-cache : fix handling when find_slot fails

ggml-ci

* kv-cache : restore find_slot impl

ggml-ci

* kv-cache : add comments

* kv-cache : add bounds checks for sequence id

ggml-ci

* cont : add n_seq_max to batch allocr

ggml-ci

* kv-cache : perform stream copies lazily after llama_synchronize

ggml-ci

* kv-cache : avoid throwing exceptions across the C boundary

ggml-ci

* CUDA: 4D FlashAttention support (#14628)

* CUDA: 4D FlashAttention support

* CUDA: fix WMMA FA kernel

* llama : rename attn_streams -> kv_unified

ggml-ci

* common : rename kv_split -> kv_unified

ggml-ci

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2025-07-16 16:35:42 +03:00
Johannes Gäßler 12a81af45f CUDA: broadcasting for FlashAttention mask (#14500) 2025-07-02 15:48:33 +03:00
Johannes Gäßler a68247439b CUDA: fix FA tg at long context for CC >= 8.9 (#13852) 2025-05-28 13:33:37 +02:00
Johannes Gäßler 6da34fa276 CUDA: faster Deepseek FA, add Turing support (#13435) 2025-05-14 16:08:20 +02:00
Johannes Gäßler 0cf6725e9f CUDA: FA support for Deepseek (Ampere or newer) (#13306)
* CUDA: FA support for Deepseek (Ampere or newer)

* do loop unrolling via C++ template
2025-05-09 13:34:58 +02:00
Johannes Gäßler 2356fb1d53 CUDA: fix bad asserts for partial offload (#13337) 2025-05-06 13:58:51 +02:00
R0CKSTAR 916c83bfe7 musa: fix compilation warnings in mp_22/31 (#12780)
Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
2025-04-06 15:23:54 +02:00
R0CKSTAR 492d7f1ff7 musa: fix all warnings, re-enable -DLLAMA_FATAL_WARNINGS=ON in ci and update doc (#12611)
* musa: fix all warnings

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

* musa: enable -DLLAMA_FATAL_WARNINGS=ON in run.sh

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

* musa: update ci doc (install ccache)

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

* fix Windows build issue

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

* Address review comments

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

* Address review comments

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

---------

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
2025-03-30 10:59:38 +02:00
Gaurav GargandJohannes Gäßler 517b5ddbf0 CUDA: Improve flash decoding kernel GPU occupancy for BS=1 case (#12183)
- Find out active blocks per SM using cudaOccupancyMaxActiveBlocksPerMultiprocessor API. Use this value to determine the optimal parallel_blocks value.
- Prefer vector flash attention kernels over MMA kernel for BS=1

Fixes Issue: #12182
---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2025-03-19 20:52:06 +01:00
uvos 34c961b181 CUDA/HIP: Fix fattn-vec-* when device warp size is not 32 (#12315)
When fattn-wmma was ported over to warp64 various bits that also touch fattn-vec where converted to
selectable warp size, however the fattn-vec kernels dont work with 64 wide warps for now, so we need
to avoid launching them with parameters for warp64
2025-03-12 10:14:11 +01:00
becade5de7 HIP: implement FlashAttention via rocWMMA for CDNA and RDNA3+ (#12032)
Adds GGML_HIP_ROCWMMA_FATTN and rocwmma header check
Adds rocWMMA support to fattn-wmma-f16

---

Signed-off-by: Carl Klemm <carl@uvos.xyz>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Ben Jackson <ben@ben.com>
2025-03-03 22:10:54 +01:00
Johannes Gäßler 5fa07c2f93 CUDA: optimize FA for GQA + large batches (#12014) 2025-02-22 12:20:17 +01:00
Johannes GäßlerandDiego Devesa 73e2ed3ce3 CUDA: use async data loading for FlashAttention (#11894)
* CUDA: use async data loading for FlashAttention

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
2025-02-17 14:03:24 +01:00
Johannes Gäßler 6eecde3cc8 HIP: fix flash_attn_stream_k_fixup warning (#11604) 2025-02-02 23:48:29 +01:00
Johannes GäßlerandDiego Devesa 864a0b67a6 CUDA: use mma PTX instructions for FlashAttention (#11583)
* CUDA: use mma PTX instructions for FlashAttention

* __shfl_sync workaround for movmatrix

* add __shfl_sync to HIP

Co-authored-by: Diego Devesa <slarengh@gmail.com>
2025-02-02 19:31:09 +01:00
ae8de6d50a ggml : build backends as libraries (#10256)
* ggml : build backends as libraries

---------

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: R0CKSTAR <xiaodong.ye@mthreads.com>
2024-11-14 18:04:35 +01:00
Johannes Gäßler e11bd856d5 CPU/CUDA: Gemma 2 FlashAttention support (#8542)
* CPU/CUDA: Gemma 2 FlashAttention support

* apply logit_softcap to scale in kernel

* disable logit softcapping tests on Metal

* remove metal check
2024-08-24 21:34:59 +02:00