CUDA: add implicit conv3d by bssrdf · Pull Request #16948 · ggml-org/llama.cpp

bssrdf · 2025-11-02T17:38:04Z

This PR adds an implicit conv3d op in CUDA backend, as a complement to IM2COL_3D+GEMM kernel currently used in SD.cpp for video models. It pretty much follows conv2d_implicit.

Using tensor cores, this PR outperforms IM2COL_3D+GEMM. The memory saving is significant, as expected.

ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes

(IC, OC, IW, IH, ID, KW, KH, KD)	im2col_3D+GEMM TIME	im2col_3D+GEMM VRAM	implicit GEMM TIME	implicit GEMM VRAM	pytorch/cudnn TIME
(320, 1280, 26, 38, 8, 3, 3, 3)	1.81 ms	168.85 MB	1.53 ms	38.59 MB	1.42 ms
(1280, 1280, 26, 38, 8, 3, 3, 3)	6.71 ms	559.61 MB	6.01 ms	38.59 MB	5.68 ms
(320, 1280, 52, 76, 8, 3, 3, 3)	7.39 ms	675.39 MB	3.86 ms	154.38 MB	5.64 ms
(1280, 1280, 52, 76, 8, 3, 3, 3)	26.40 ms	2238.44 MB	15.25 ms	154.38 MB	21.62 ms
(320, 1280, 104, 152, 8, 3, 3, 3)	29.36 ms	2701.56 MB	15.42 ms	617.50 MB	22.02 ms
(1280, 1280, 104, 152, 8, 3, 3, 3)	105.70 ms	8953.75 MB	60.83 ms	617.50 MB	82.19 ms
(320, 1280, 208, 304, 4, 3, 3, 3)	59.02 ms	5403.12 MB	30.06 ms	1235.00 MB	43.78 ms
(640, 1280, 208, 304, 4, 3, 3, 3)	109.66 ms	9571.25 MB	60.04 ms	1235.00 MB	83.66 ms

…a backend

… 3x slower than im2col

Green-Sky · 2025-11-02T17:54:40Z

Quick n dirty test perf run:

$ result/bin/test-conv3d
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 2070, compute capability 7.5, VMM: yes

(IC, OC, IW, IH, ID, KW, KH, KD)	im2col+GEMM TIME	im2col+GEMM VRAM	implicit GEMM TIME	implicit GEMM VRAM
(320, 1280, 26, 38, 8, 3, 3, 3)	10.30 ms	168.85 MB	34.95 ms	38.59 MB
(1280, 1280, 26, 38, 8, 3, 3, 3)	37.30 ms	559.61 MB	141.30 ms	38.59 MB
(320, 1280, 52, 76, 8, 3, 3, 3)	37.85 ms	675.39 MB	137.66 ms	154.38 MB
(1280, 1280, 52, 76, 8, 3, 3, 3)	156.93 ms	2238.44 MB	558.15 ms	154.38 MB
(320, 1280, 104, 152, 8, 3, 3, 3)	157.53 ms	2701.56 MB	569.28 ms	617.50 MB

ggml_backend_cuda_buffer_type_alloc_buffer: allocating 8953.75 MiB on device 0: cudaMalloc failed: out of memory
ggml_gallocr_reserve_n: failed to allocate CUDA0 buffer of size 9388687360
Segmentation fault (core dumped)

bssrdf · 2025-11-02T18:29:20Z

Quick n dirty test perf run:
$ result/bin/test-conv3d
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 2070, compute capability 7.5, VMM: yes
(IC, OC, IW, IH, ID, KW, KH, KD) im2col+GEMM TIME im2col+GEMM VRAM implicit GEMM TIME implicit GEMM VRAM
(320, 1280, 26, 38, 8, 3, 3, 3) 10.30 ms 168.85 MB 34.95 ms 38.59 MB
(1280, 1280, 26, 38, 8, 3, 3, 3) 37.30 ms 559.61 MB 141.30 ms 38.59 MB
(320, 1280, 52, 76, 8, 3, 3, 3) 37.85 ms 675.39 MB 137.66 ms 154.38 MB
(1280, 1280, 52, 76, 8, 3, 3, 3) 156.93 ms 2238.44 MB 558.15 ms 154.38 MB
(320, 1280, 104, 152, 8, 3, 3, 3) 157.53 ms 2701.56 MB 569.28 ms 617.50 MB
ggml_backend_cuda_buffer_type_alloc_buffer: allocating 8953.75 MiB on device 0: cudaMalloc failed: out of memory
ggml_gallocr_reserve_n: failed to allocate CUDA0 buffer of size 9388687360
Segmentation fault (core dumped)

Need to get tensors working:)

Green-Sky · 2025-11-02T21:37:12Z

With that last commit I am catching an illegal memory access.

[DEBUG] ggml_extend.hpp:1588 - wan_vae compute buffer size: 715.16 MB(VRAM)
[ERROR] ggml_extend.hpp:75   - ggml_cuda_compute_forward: IM2COL failed
[ERROR] ggml_extend.hpp:75   - CUDA error: an illegal memory access was encountered
[ERROR] ggml_extend.hpp:75   -   current device: 0, in function ggml_cuda_compute_forward at /build/fr4p6mskfb7pv7d6n87kfpipj5dr1bql-source/ggml/src/ggml-cuda/ggml-cuda.cu:2544
[ERROR] ggml_extend.hpp:75   -   err
/build/fr4p6mskfb7pv7d6n87kfpipj5dr1bql-source/ggml/src/ggml-cuda/ggml-cuda.cu:89: CUDA error

bssrdf · 2025-11-02T22:31:43Z

With that last commit I am catching an illegal memory access.

[DEBUG] ggml_extend.hpp:1588 - wan_vae compute buffer size: 715.16 MB(VRAM)
[ERROR] ggml_extend.hpp:75   - ggml_cuda_compute_forward: IM2COL failed
[ERROR] ggml_extend.hpp:75   - CUDA error: an illegal memory access was encountered
[ERROR] ggml_extend.hpp:75   -   current device: 0, in function ggml_cuda_compute_forward at /build/fr4p6mskfb7pv7d6n87kfpipj5dr1bql-source/ggml/src/ggml-cuda/ggml-cuda.cu:2544
[ERROR] ggml_extend.hpp:75   -   err
/build/fr4p6mskfb7pv7d6n87kfpipj5dr1bql-source/ggml/src/ggml-cuda/ggml-cuda.cu:89: CUDA error

Give it another try. Tensor core path should be on and working.

ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes

(IC, OC, IW, IH, ID, KW, KH, KD)	im2col+GEMM TIME	im2col+GEMM VRAM	implicit GEMM TIME	implicit GEMM VRAM
(320, 1280, 26, 38, 8, 3, 3, 3)	1.88 ms	168.85 MB	1.54 ms	38.59 MB
(1280, 1280, 26, 38, 8, 3, 3, 3)	6.76 ms	559.61 MB	6.18 ms	38.59 MB
(320, 1280, 52, 76, 8, 3, 3, 3)	7.58 ms	675.39 MB	3.99 ms	154.38 MB
(1280, 1280, 52, 76, 8, 3, 3, 3)	26.99 ms	2238.44 MB	15.51 ms	154.38 MB
(320, 1280, 104, 152, 8, 3, 3, 3)	30.06 ms	2701.56 MB	15.86 ms	617.50 MB
(1280, 1280, 104, 152, 8, 3, 3, 3)	108.33 ms	8953.75 MB	62.23 ms	617.50 MB
(320, 1280, 208, 304, 4, 3, 3, 3)	60.33 ms	5403.12 MB	30.79 ms	1235.00 MB
(640, 1280, 208, 304, 4, 3, 3, 3)	112.34 ms	9571.25 MB	61.49 ms	1235.00 MB

Green-Sky · 2025-11-02T23:14:22Z

Looks good!

$ result/bin/test-conv3d
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 2070, compute capability 7.5, VMM: yes

(IC, OC, IW, IH, ID, KW, KH, KD)	im2col+GEMM TIME	im2col+GEMM VRAM	implicit GEMM TIME	implicit GEMM VRAM
(320, 1280, 26, 38, 8, 3, 3, 3)	11.35 ms	168.85 MB	6.15 ms	38.59 MB
(1280, 1280, 26, 38, 8, 3, 3, 3)	39.40 ms	559.61 MB	24.59 ms	38.59 MB
(320, 1280, 52, 76, 8, 3, 3, 3)	40.52 ms	675.39 MB	23.23 ms	154.38 MB
(1280, 1280, 52, 76, 8, 3, 3, 3)	166.10 ms	2238.44 MB	91.93 ms	154.38 MB
(320, 1280, 104, 152, 8, 3, 3, 3)	164.08 ms	2701.56 MB	91.41 ms	617.50 MB

ggml_backend_cuda_buffer_type_alloc_buffer: allocating 8953.75 MiB on device 0: cudaMalloc failed: out of memory
ggml_gallocr_reserve_n: failed to allocate CUDA0 buffer of size 9388687360
Segmentation fault (core dumped)

jeffbolznv · 2025-11-02T23:24:50Z

Please add the perf tests to make_test_cases_perf in test-backend-ops.cpp.

bssrdf · 2025-11-02T23:44:20Z

Looks good!
$ result/bin/test-conv3d
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 2070, compute capability 7.5, VMM: yes
(IC, OC, IW, IH, ID, KW, KH, KD) im2col+GEMM TIME im2col+GEMM VRAM implicit GEMM TIME implicit GEMM VRAM
(320, 1280, 26, 38, 8, 3, 3, 3) 11.35 ms 168.85 MB 6.15 ms 38.59 MB
(1280, 1280, 26, 38, 8, 3, 3, 3) 39.40 ms 559.61 MB 24.59 ms 38.59 MB
(320, 1280, 52, 76, 8, 3, 3, 3) 40.52 ms 675.39 MB 23.23 ms 154.38 MB
(1280, 1280, 52, 76, 8, 3, 3, 3) 166.10 ms 2238.44 MB 91.93 ms 154.38 MB
(320, 1280, 104, 152, 8, 3, 3, 3) 164.08 ms 2701.56 MB 91.41 ms 617.50 MB
ggml_backend_cuda_buffer_type_alloc_buffer: allocating 8953.75 MiB on device 0: cudaMalloc failed: out of memory
ggml_gallocr_reserve_n: failed to allocate CUDA0 buffer of size 9388687360
Segmentation fault (core dumped)

Please add the perf tests to make_test_cases_perf in test-backend-ops.cpp.

Will do. Right now, test-backend-op test -o CONV_3D failed to pass.

@Green-Sky , does the WAN video generation work ok?

Green-Sky · 2025-11-02T23:52:51Z

@Green-Sky , does the WAN video generation work ok?

As far as I can tell yes. I have only tested Wan2.2-TI2V-5B yet (wan2.2 vae).

Single "frame" (aka image gen using wan):

I don't have the im2col gemm image ready, but it looked identical. Same for 5 frames.

I was unable to run higher frame counts due to my 8gig vram limit. It should technically just fit if I restart and don't open extra programs. Not gonna do that today.

Green-Sky · 2025-11-03T00:14:13Z

@Green-Sky , does the WAN video generation work ok?

I tried a smaller resolution and I did get another illegal memory access:

[DEBUG] ggml_extend.hpp:1588 - wan_vae compute buffer size: 5393.97 MB(VRAM)
[ERROR] ggml_extend.hpp:75   - ggml_cuda_compute_forward: CONCAT failed
[ERROR] ggml_extend.hpp:75   - CUDA error: an illegal memory access was encountered
[ERROR] ggml_extend.hpp:75   -   current device: 0, in function ggml_cuda_compute_forward at /build/jj5n0s93mbksd9wmv6vrs8dxmhcvhnik-source/ggml/src/ggml-cuda/ggml-cuda.cu:2544
[ERROR] ggml_extend.hpp:75   -   err

bssrdf · 2025-11-03T13:51:04Z

@Green-Sky , does the WAN video generation work ok?

I tried a smaller resolution and I did get another illegal memory access:

[DEBUG] ggml_extend.hpp:1588 - wan_vae compute buffer size: 5393.97 MB(VRAM)
[ERROR] ggml_extend.hpp:75   - ggml_cuda_compute_forward: CONCAT failed
[ERROR] ggml_extend.hpp:75   - CUDA error: an illegal memory access was encountered
[ERROR] ggml_extend.hpp:75   -   current device: 0, in function ggml_cuda_compute_forward at /build/jj5n0s93mbksd9wmv6vrs8dxmhcvhnik-source/ggml/src/ggml-cuda/ggml-cuda.cu:2544
[ERROR] ggml_extend.hpp:75   -   err

@Green-Sky, can you retry my latest commit? I see your test failed at CONCAT op. That's weird. Could you also run this case with current main branch? Thanks.

Green-Sky · 2025-11-03T16:54:33Z

@leejet I suspect sd.cpp is doing something wrong here, because if width and height are multiples of 32, decoding works. But if they are just multiples of 16 I get that illegal memory access error.

But it is still possible that it is an error with this pr.

Green-Sky · 2025-11-03T17:07:34Z

$ result/bin/sd -M vid_gen --diffusion-model models/wan/Wan2.2-TI2V-5B-Q8_0.gguf --vae models/wan/Wan2.2_VAE.safetensors --t5xxl models/wan/umt5-xxl-encoder-Q8_0.gguf -p "a lovely cat strolling down a wooden plank" --cfg-scale 6 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 576 -H 352 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0 --steps 48

wan2.2-TI2V-5B-Q8_0-cat-strolling_funkysize.mp4

bssrdf · 2025-11-09T22:31:41Z

@leejet I suspect sd.cpp is doing something wrong here, because if width and height are multiples of 32, decoding works. But if they are just multiples of 16 I get that illegal memory access error.

But it is still possible that it is an error with this pr.

@Green-Sky, I just fixed a bug which may be related to your crash. Please give it a try. Thanks,

Green-Sky · 2025-11-10T11:05:54Z

@leejet I suspect sd.cpp is doing something wrong here, because if width and height are multiples of 32, decoding works. But if they are just multiples of 16 I get that illegal memory access error.
But it is still possible that it is an error with this pr.

@Green-Sky, I just fixed a bug which may be related to your crash. Please give it a try. Thanks,

Yes, I can indeed now use multiples of 16, that are not multiples of 32. 🚀

wan2.2-TI2V-5B-Q8_0-cat-strolling_funkysize_16.mp4

Green-Sky · 2025-11-10T13:49:08Z

I switched to the fastwan 2.2 TI2V 5B model and generated using 6 steps the most frames+res I could fit.

576x352 93frames

fastwan2.2-t2i5b_cat1-steps6.mp4

... in 56 seconds total. On CPU this would take probably over 1 hour.

bssrdf · 2025-11-10T14:31:43Z

I switched to the fastwan 2.2 TI2V 5B model and generated using 6 steps the most frames+res I could fit.

576x352 93frames

fastwan2.2-t2i5b_cat1-steps6.mp4
... in 56 seconds total. On CPU this would take probably over 1 hour.

@Green-Sky , can you compare the performance IM2COL_3D vs IMPLICIT GEMM? Thanks.

Green-Sky · 2025-11-10T14:37:46Z

@Green-Sky , can you compare the performance IM2COL_3D vs IMPLICIT GEMM? Thanks.

With sd.cpp that is rather impractical for me, I dont have 20Gigs of vram 😄. I can only run a limited selection of the tests that you provide.

bssrdf · 2025-11-10T15:22:17Z

@Green-Sky , can you compare the performance IM2COL_3D vs IMPLICIT GEMM? Thanks.

With sd.cpp that is rather impractical for me, I dont have 20Gigs of vram 😄. I can only run a limited selection of the tests that you provide.

Sure. You have done a lot of great jobs testing these PRs in sd.cpp. Many thanks.

Green-Sky · 2025-11-10T15:41:43Z

@Green-Sky , can you compare the performance IM2COL_3D vs IMPLICIT GEMM? Thanks.

With sd.cpp that is rather impractical for me, I dont have 20Gigs of vram 😄. I can only run a limited selection of the tests that you provide.

Sure. You have done a lot of great jobs testing these PRs in sd.cpp. Many thanks.

I will redo this one later #16948 (comment) , but as you can see it did not complete it.

:)

Green-Sky · 2025-11-10T22:23:27Z

Was experimenting more with wan and got another of those illegal access errors in CONCAT. But I am not latest, so I will check latest.

-W 608 -H 368 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.5 --steps 6 (among others)
sample 38x23x9

edit: It has indeed been fixed somewhere in the last few commits: :)

Green-Sky · 2025-11-11T21:47:11Z

You changed the test selection again :P

Anyway, here is that one test:

$ result/bin/test-conv3d
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 2070, compute capability 7.5, VMM: yes

(IC, OC, IW, IH, ID, KW, KH, KD)	im2col+GEMM TIME	im2col+GEMM VRAM	implicit GEMM TIME	implicit GEMM VRAM
(320, 320, 104, 158, 8, 3, 3, 3)	110.58 ms	2326.80 MB	34.35 ms	160.47 MB

edit: Can you keep them enabled, and not commit your last selection? Also, would be really cool if they where sorted a bit by vram use by im2col+gemm. That would really help bench this on more hardware.

bssrdf · 2025-11-11T23:28:18Z

You changed the test selection again :P

Anyway, here is that one test:
$ result/bin/test-conv3d
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA GeForce RTX 2070, compute capability 7.5, VMM: yes
(IC, OC, IW, IH, ID, KW, KH, KD) im2col+GEMM TIME im2col+GEMM VRAM implicit GEMM TIME implicit GEMM VRAM
(320, 320, 104, 158, 8, 3, 3, 3) 110.58 ms 2326.80 MB 34.35 ms 160.47 MB
edit: Can you keep them enabled, and not commit your last selection? Also, would be really cool if they where sorted a bit by vram use by im2col+gemm. That would really help bench this on more hardware.

Sorry, I am just adding more test cases (obtained from sd.cpp wan generation). I want to test these real world cases instead of makeup ones. But in my commit I am going to turn most off because CI will time out otherwise. You can always easily turn them on by uncommenting them.

CarlGao4 · 2025-12-09T03:02:59Z

This PR hasn't been updated for a month. Any news?

Green-Sky · 2025-12-09T10:31:32Z

It has been working great for me.
So all green here. :)

CarlGao4 · 2025-12-12T02:43:44Z

Can you fork latest sd.cpp with this patch? I can't build it successfully

bssrdf · 2025-12-12T14:50:38Z

Can you fork latest sd.cpp with this patch? I can't build it successfully

To make it work in SD, you can just copy the two new files(conv3d-implicit.cu and conv3d-implicit.cuh) into ggml-cuda/ and change a few lines in ggml-cuda.cu. That's it.

Green-Sky · 2025-12-12T18:29:23Z

Can you fork latest sd.cpp with this patch? I can't build it successfully

To make it work in SD, you can just copy the two new files(conv3d-implicit.cu and conv3d-implicit.cuh) into ggml-cuda/ and change a few lines in ggml-cuda.cu. That's it.

With a simple sd.cpp modification I point out here: leejet/stable-diffusion.cpp#872 (comment)
Otherwise it will not be used. (:

CarlGao4 · 2025-12-13T12:00:37Z

I tried these steps before, but finally it telled me something that op not found (I've deleted the files so I can't tell you the exact output)

bssrdf · 2026-10-06T20:05:55Z

Close this.

bssrdf added 5 commits November 1, 2025 20:08

use conv2d_implicit as template; add conv3d parameters

ab15f6c

WIP: updating indices for input and kernel; enable OP_CONV_3D for cud…

52455b8

…a backend

WIP: build ok

0a64ea8

conv3d WIP: added a test case

e802036

conv3D WIP: fixed a launch param bug, results now correct; performace…

a5b68bc

… 3x slower than im2col

bssrdf requested review from ggerganov and slaren as code owners November 2, 2025 17:38

bssrdf marked this pull request as draft November 2, 2025 17:38

github-actions Bot added testing Everything test related Nvidia GPU Issues specific to Nvidia GPUs ggml changes relating to the ggml tensor library for machine learning labels Nov 2, 2025

Green-Sky mentioned this pull request Nov 2, 2025

The Process of Wan2.1 t2v 1.3B VAE Decoding needs too much VRAM/RAM??? leejet/stable-diffusion.cpp#872

Open

conv3d WIP: turn on tensor cores; NCDHW2NDHWC to be worked out

3f5c504

conv3d WIP: enabled tensor core path

3308cce

fixed a bug now all test cases passed

2357922

bssrdf marked this pull request as ready for review November 3, 2025 14:00

bssrdf added 4 commits November 3, 2025 09:28

make CI happy

5aa4ae7

one more CI fix

91650b7

add some test cases in test-backend-op perf

f0ced9f

avoid CI time out on test-conv3d

f921286

fixed a bug in calculating filter row index

d2d814c

bssrdf and others added 5 commits November 10, 2025 13:13

fuse cast to float into conv epilogue; improve swizzling for output

a428fee

added split-k mode to tensor core path

15daa5a

updated test cases using tensor shapes from sd.cpp wan video generation

89103a8

add a case havign memory access violation

5e1352c

fixed a bug of not bound checking batch dimension

d6d2448

Green-Sky mentioned this pull request Nov 14, 2025

CUDA: add CONV_3D operator support #17255

Open

bssrdf mentioned this pull request Dec 1, 2025

Feature Request: Add Video Modality Support (Qwen2.5-VL) via llama-mtmd-cli #17660

Closed

4 tasks done

Green-Sky mentioned this pull request Jun 13, 2026

CUDA: Add conv3d. #24569

Closed

leejet mentioned this pull request Sep 19, 2026

cuda : add conv3d with implicit GEMM #29137

Merged

bssrdf closed this Oct 6, 2026

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bssrdf commented Nov 2, 2025 •

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bssrdf commented Dec 12, 2025 •

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