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PascalandAman Gupta 58e68df0f9 cuda: fuse snake activation (mul, sin, sqr, mul, add) (#22667) 
* cuda: fuse snake activation (mul, sin, sqr, mul, add)

Add ggml_cuda_op_snake_fused with F32 / F16 / BF16 templates. The
matcher recognizes the naive 5 op decomposition emitted by audio
decoders (BigVGAN, Vocos) for snake activation
y = x + sin(a*x)^2 * inv_b and rewrites it to a single elementwise
kernel.

Add test_snake_fuse comparing CPU naive vs CUDA fused across
F32 / F16 / BF16.

* cuda: address review feedback from @am17an

Use ggml_cuda_cast for F32/F16/BF16 conversions and rename
kernel_snake to snake_kernel to match upstream conventions.

* cuda: snake fusion fastdiv on T_len, Suggested-by: @am17an

* Update tests/test-backend-ops.cpp

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

* cuda: snake fusion check add->type matches x->type

Address review feedback from @am17an

* cuda: snake fusion check add->type matches x->type

Moved for readability (equivalent)
Address review feedback from @am17an

---------

Co-authored-by: Aman Gupta <amangupta052@gmail.com>
2026-05-08 17:44:09 +08:00
..
template-instances
ggml-cuda: add flash-attn support for DKQ=320/DV=256 with ncols2=32 (… (#22286)
2026-04-28 21:37:35 +02:00
vendors
CUDA: batch out_prod inner loop with cublasSgemmStridedBatched (#22651)
2026-05-07 21:59:29 +02:00
acc.cu
llama/ggml: add LLM training support (#10544)
2025-05-12 14:44:49 +02:00
acc.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
add-id.cu
musa: fix build warnings (#15258)
2025-08-20 10:17:37 +08:00
add-id.cuh
llama : add gpt-oss (#15091)
2025-08-05 22:10:36 +03:00
arange.cu
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
arange.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
argmax.cu
ggml : use WARP_SIZE/2 for argmax reduction offset (#18092)
2025-12-17 11:47:01 +08:00
argmax.cuh
ggml/ex: calculate accuracy in graph, adapt MNIST (ggml/980)
2024-10-03 21:17:26 +03:00
argsort.cu
CUDA: Limit DeviceSegmentedSort to immediate mode (#21718)
2026-04-13 11:14:06 +02:00
argsort.cuh
sampling : add support for backend sampling (#17004)
2026-01-04 22:22:16 +02:00
binbcast.cu
CUDA: fuse muls (#21665)
2026-04-10 10:24:09 +08:00
binbcast.cuh
CUDA: fuse muls (#21665)
2026-04-10 10:24:09 +08:00
clamp.cu
cuda: unary ops as float + de-duplicate (ggml/1130)
2025-03-03 18:18:11 +02:00
clamp.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
CMakeLists.txt
ggml: backend-agnostic tensor parallelism (experimental) (#19378)
2026-04-09 16:42:19 +02:00
common.cuh
ggml-cuda: Repost of 21896: Blackwell native NVFP4 support (#22196)
2026-04-29 06:47:42 +08:00
concat.cu
CUDA: better coalesce data-access for contiguous concat (#22330)
2026-04-26 09:21:45 +02:00
concat.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
conv2d-dw.cu
CUDA: add conv_2d_dw (#14265)
2025-06-20 09:50:24 +08:00
conv2d-dw.cuh
CUDA: add conv_2d_dw (#14265)
2025-06-20 09:50:24 +08:00
conv2d-transpose.cu
CUDA & CPU: support F32 kernel type for CONV_TRANSPOSE_2D (#17094)
2026-03-26 10:19:14 +08:00
conv2d-transpose.cuh
CUDA & CPU: support F32 kernel type for CONV_TRANSPOSE_2D (#17094)
2026-03-26 10:19:14 +08:00
conv2d.cu
CUDA: fix build error from ambiguous __half conversions in conv2d (#15690)
2025-09-01 06:55:06 +05:30
conv2d.cuh
CUDA: add conv2d (#15635)
2025-08-28 20:33:03 +02:00
conv-transpose-1d.cu
musa: add GGML_UNUSED_VARS (#15446)
2025-08-21 11:06:05 +08:00
conv-transpose-1d.cuh
feat: cuda implementation for ggml_conv_transpose_1d (ggml/854)
2024-07-08 12:23:00 +03:00
convert.cu
cuda: Q1_0 initial backend (#21629)
2026-04-15 18:38:38 +02:00
convert.cuh
CUDA: fix BF16 FA compilation (#20865)
2026-03-22 17:53:33 +01:00
count-equal.cu
ggml: fix zero division in ‘dne’ calculation in CUDA COUNT_EQUAL operator when ‘ne’ is small (#10213)
2024-11-09 08:35:46 +01:00
count-equal.cuh
ggml/ex: calculate accuracy in graph, adapt MNIST (ggml/980)
2024-10-03 21:17:26 +03:00
cp-async.cuh
CUDA: FA support for Deepseek (Ampere or newer) (#13306)
2025-05-09 13:34:58 +02:00
cpy-utils.cuh
cuda : support non-contiguous i32 to i32 copy (#17326)
2025-11-23 11:13:34 +01:00
cpy.cu
Fix data race in CUDA's "cpy" kernel (influences GGML's DUP, CONT operations). (#20507)
2026-03-14 13:19:44 +08:00
cpy.cuh
cuda : remove legacy copy-op pointer indirection code (#16485)
2025-10-14 11:53:49 +02:00
cross-entropy-loss.cu
CUDA: add dynamic shared mem to softmax, refactor general usage (#14497)
2025-07-03 07:45:11 +08:00
cross-entropy-loss.cuh
ggml/examples: add backend support for numerical optimization (ggml/949)
2024-09-20 21:15:05 +03:00
cumsum.cu
sampling : add support for backend sampling (#17004)
2026-01-04 22:22:16 +02:00
cumsum.cuh
Add support for CUMSUM and TRI for CUDA. (#17584)
2025-12-04 22:19:51 +01:00
dequantize.cuh
cuda: Q1_0 initial backend (#21629)
2026-04-15 18:38:38 +02:00
diag.cu
Add DIAG for CUDA (#17873)
2025-12-09 20:28:57 +01:00
diag.cuh
Add DIAG for CUDA (#17873)
2025-12-09 20:28:57 +01:00
diagmask.cu
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
diagmask.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
fattn-common.cuh
[CUDA ] Write an optimized flash_attn_stream_k_fixup kernel (#21159)
2026-04-06 20:34:29 +02:00
fattn-mma-f16.cuh
ggml-cuda: add flash-attn support for DKQ=320/DV=256 with ncols2=32 (… (#22286)
2026-04-28 21:37:35 +02:00
fattn-tile.cu
ggml-cuda: add flash-attn support for DKQ=320/DV=256 with ncols2=32 (… (#22286)
2026-04-28 21:37:35 +02:00
fattn-tile.cuh
CUDA: fix tile FA kernel on Pascal (#22541)
2026-04-30 13:04:50 +02:00
fattn-vec.cuh
ggml-cuda: native bf16 flash attention for vec kernel (#20525)
2026-03-22 11:05:51 +01:00
fattn-wmma-f16.cu
Adjust workaround for ROCWMMA_FATTN/GFX9 to only newer ROCm veresions (#19591)
2026-02-16 14:46:08 +01:00
fattn-wmma-f16.cuh
chore : correct typos [no ci] (#20041)
2026-03-05 08:50:21 +01:00
fattn.cu
ggml-cuda: add flash-attn support for DKQ=320/DV=256 with ncols2=32 (… (#22286)
2026-04-28 21:37:35 +02:00
fattn.cuh
CUDA: refactor FA support/selection code (#15454)
2025-08-20 23:14:14 +02:00
fill.cu
ggml : allow fill node alloc inplace (#17870)
2025-12-09 12:23:47 +01:00
fill.cuh
cuda : add FILL op support (#17851)
2025-12-08 21:10:12 +08:00
gated_delta_net.cu
CUDA: GDN hide memory latency (#20537)
2026-03-16 11:41:45 +08:00
gated_delta_net.cuh
ggml: add GATED_DELTA_NET op (#19504)
2026-03-07 15:41:10 +08:00
getrows.cu
CUDA: use fastdiv for batch index split in get_rows (#22650)
2026-05-04 16:24:05 +02:00
getrows.cuh
CUDA: batched+noncont MMQ, refactor bs>1 MoE code (#13199)
2025-04-30 23:12:59 +02:00
ggml-cuda.cu
cuda: fuse snake activation (mul, sin, sqr, mul, add) (#22667)
2026-05-08 17:44:09 +08:00
gla.cu
llama: add support for QRWKV6 model architecture (#11001)
2025-01-10 09:58:08 +08:00
gla.cuh
llama: add support for QRWKV6 model architecture (#11001)
2025-01-10 09:58:08 +08:00
im2col.cu
CUDA: fix im2col_3d to respect non-contiguous inputs (views) (#15956)
2025-09-16 00:28:31 +02:00
im2col.cuh
ggml: add ops for WAN video model (cuda && cpu) (#15669)
2025-09-04 10:38:49 +02:00
mean.cu
ggml-cuda: enable cuda-graphs for n-cpu-moe (#18934)
2026-01-24 14:25:20 +08:00
mean.cuh
CUDA: add mean operation (#14313)
2025-06-22 12:39:54 +08:00
mma.cuh
ggml-cuda: Repost of 21896: Blackwell native NVFP4 support (#22196)
2026-04-29 06:47:42 +08:00
mmf.cu
HIP: add mmf for CDNA (#18896)
2026-01-29 11:10:53 +01:00
mmf.cuh
HIP: add mmf for CDNA (#18896)
2026-01-29 11:10:53 +01:00
mmid.cu
CUDA: add fp kernel for larger batch size MoE (#16512)
2025-10-14 13:15:15 +02:00
mmid.cuh
CUDA: add fp kernel for larger batch size MoE (#16512)
2025-10-14 13:15:15 +02:00
mmq.cu
ggml-cuda: Repost of 21896: Blackwell native NVFP4 support (#22196)
2026-04-29 06:47:42 +08:00
mmq.cuh
ggml-cuda: Repost of 21896: Blackwell native NVFP4 support (#22196)
2026-04-29 06:47:42 +08:00
mmvf.cu
CUDA: use mmvq for mul-mat-id for small batch sizes (#18958)
2026-02-03 23:31:23 +08:00
mmvf.cuh
CUDA: use mmvq for mul-mat-id for small batch sizes (#18958)
2026-02-03 23:31:23 +08:00
mmvq.cu
ggml-cuda: Repost of 21896: Blackwell native NVFP4 support (#22196)
2026-04-29 06:47:42 +08:00
mmvq.cuh
Optimize MOE GEMV kernel for BS > 1. (#20905)
2026-03-29 18:35:18 +02:00
norm.cu
CUDA: Factor out and re-use block_reduce function (#18785)
2026-01-15 10:44:54 +08:00
norm.cuh
CUDA: fuse adds, fuse add with rms norm (#15631)
2025-08-29 11:35:58 +08:00
opt-step-adamw.cu
ggml: new optimization interface (ggml/988)
2024-11-17 08:30:29 +02:00
opt-step-adamw.cuh
ggml/examples: add backend support for numerical optimization (ggml/949)
2024-09-20 21:15:05 +03:00
opt-step-sgd.cu
finetune: SGD optimizer, more CLI args (#13873)
2025-08-14 12:03:57 +02:00
opt-step-sgd.cuh
finetune: SGD optimizer, more CLI args (#13873)
2025-08-14 12:03:57 +02:00
out-prod.cu
CUDA: batch out_prod inner loop with cublasSgemmStridedBatched (#22651)
2026-05-07 21:59:29 +02:00
out-prod.cuh
ggml/examples: add backend support for numerical optimization (ggml/949)
2024-09-20 21:15:05 +03:00
pad_reflect_1d.cu
musa: fix build warnings (#15611)
2025-09-26 02:56:10 +02:00
pad_reflect_1d.cuh
cuda : add Pad Reflect 1D support (#14659)
2025-08-22 13:06:29 +02:00
pad.cu
cuda : extend GGML_OP_PAD to work with non-cont src0 (#19429)
2026-02-10 08:07:16 +02:00
pad.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
pool2d.cu
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
pool2d.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
quantize.cu
ggml-cuda: Repost of 21896: Blackwell native NVFP4 support (#22196)
2026-04-29 06:47:42 +08:00
quantize.cuh
ggml-cuda: Repost of 21896: Blackwell native NVFP4 support (#22196)
2026-04-29 06:47:42 +08:00
reduce_rows.cuh
CUDA: Factor out and re-use block_reduce function (#18785)
2026-01-15 10:44:54 +08:00
roll.cu
CUDA: add roll (#14919)
2025-07-29 14:45:18 +08:00
roll.cuh
CUDA: add roll (#14919)
2025-07-29 14:45:18 +08:00
rope.cu
CUDA: Fix non-contig rope (#19338)
2026-02-08 15:12:51 +02:00
rope.cuh
CUDA: fuse rope + set_rows (#16884)
2025-11-13 08:50:01 +08:00
scale.cu
ggml: add ops for WAN video model (cuda && cpu) (#15669)
2025-09-04 10:38:49 +02:00
scale.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
set-rows.cu
CUDA: use fastdiv in set-rows (#16834)
2025-10-29 21:11:53 +08:00
set-rows.cuh
CUDA: add set rows for f32 and f16 (#14551)
2025-07-12 16:31:38 +03:00
set.cu
cuda: add SET operation support (#16804)
2025-10-28 20:10:28 +01:00
set.cuh
cuda: add SET operation support (#16804)
2025-10-28 20:10:28 +01:00
snake.cu
cuda: fuse snake activation (mul, sin, sqr, mul, add) (#22667)
2026-05-08 17:44:09 +08:00
snake.cuh
cuda: fuse snake activation (mul, sin, sqr, mul, add) (#22667)
2026-05-08 17:44:09 +08:00
softcap.cu
cuda : add softcap fusion (#14907)
2025-07-29 14:22:03 +02:00
softcap.cuh
cuda : add softcap fusion (#14907)
2025-07-29 14:22:03 +02:00
softmax.cu
chore : correct typos [no ci] (#20041)
2026-03-05 08:50:21 +01:00
softmax.cuh
CUDA: backwards pass for misc. ops, add tests (#11257)
2025-01-16 16:43:38 +01:00
solve_tri.cu
chore : correct typos [no ci] (#20041)
2026-03-05 08:50:21 +01:00
solve_tri.cuh
SOLVE_TRI CUDA kernel for small matrices (#17457)
2025-11-28 12:15:32 +08:00
ssm-conv.cu
CUDA: fuse SSM_CONV + ADD(bias) + SILU (#22478)
2026-04-30 02:39:56 +08:00
ssm-conv.cuh
CUDA: fuse SSM_CONV + ADD(bias) + SILU (#22478)
2026-04-30 02:39:56 +08:00
ssm-scan.cu
ggml : optimize cuda ssm_scan using warp-level reduction (#18505)
2026-01-07 02:24:34 +08:00
ssm-scan.cuh
ggml : faster ssm scan (#10558)
2025-03-31 18:05:13 +02:00
sum.cu
CUDA: Optimize reduce_rows_f32 kernel, leading up to 25x perf improvement on kernel-level and 10% perf increase for Gemma3n (#15132)
2025-08-13 10:04:46 +02:00
sum.cuh
tests: add gradient tests for all backends (ggml/932)
2024-09-08 11:05:55 +03:00
sumrows.cu
CUDA: Optimize reduce_rows_f32 kernel, leading up to 25x perf improvement on kernel-level and 10% perf increase for Gemma3n (#15132)
2025-08-13 10:04:46 +02:00
sumrows.cuh
CUDA: add mean operation (#14313)
2025-06-22 12:39:54 +08:00
top-k.cu
ggml : check return value of CUB calls used in argsort and top-k (they all return cudaError_t) (#21676)
2026-04-09 21:17:11 +08:00
top-k.cuh
sampling : add support for backend sampling (#17004)
2026-01-04 22:22:16 +02:00
topk-moe.cu
ggml-cuda: add mem check for fusion (#19916)
2026-03-07 00:05:43 +08:00
topk-moe.cuh
CUDA: refactor topk-moe to enable more models (GLM 4.7, Nemotron etc.) (#19126)
2026-01-29 10:31:28 +08:00
tri.cu
Add support for CUMSUM and TRI for CUDA. (#17584)
2025-12-04 22:19:51 +01:00
tri.cuh
Add support for CUMSUM and TRI for CUDA. (#17584)
2025-12-04 22:19:51 +01:00
tsembd.cu
ggml : fix padding in timestep embedding kernels (#15932)
2025-09-16 15:25:57 +02:00
tsembd.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
unary.cu
CUDA: fuse relu + sqr (#22249)
2026-04-23 10:28:56 +08:00
unary.cuh
CUDA: fuse relu + sqr (#22249)
2026-04-23 10:28:56 +08:00
upscale.cu
model: LFM2-VL fixes (#17577)
2025-11-30 21:57:31 +01:00
upscale.cuh
llama : reorganize source code + improve CMake (#8006)
2024-06-26 18:33:02 +03:00
vecdotq.cuh
cuda: Q1_0 initial backend (#21629)
2026-04-15 18:38:38 +02:00
wkv.cu
llama: Add support for RWKV v7 architecture (#12412)
2025-03-18 07:27:50 +08:00
wkv.cuh
llama: Add support for RWKV v7 architecture (#12412)
2025-03-18 07:27:50 +08:00