Vulkan k-quant mmq and ggml-backend offload functionality #6155

0cc4m · 2024-03-19T09:44:51Z

I added k-quant mmq shaders and cleaned up the cpu-assist functions, as they are now replaced with the ggml-backend code.

I also fixed the working buffer allocation code, it should now use noticeably less VRAM.

Should hopefully fix #5848

…ements

Clean up cpu assist code, replaced with ggml-backend offload function

slaren · 2024-03-19T12:27:18Z

Note: this is on master, I was trying to test it there first as a baseline, but the same happens on the PR.

I have a 3080 and a 3090 Ti, and I am having some trouble getting ggml-vulkan to use the 3090 Ti. It looks like it only finds the 3080.

ggml_vulkan: Found 1 Vulkan devices:
Vulkan0: NVIDIA GeForce RTX 3080 | uma: 0 | fp16: 1 | warp size: 32

vulkaninfo lists both GPUs:


Layers: count = 11
==================
VK_LAYER_KHRONOS_profiles (Khronos Profiles layer) Vulkan version 1.3.275, layer version 1:
	Layer Extensions: count = 0
	Devices: count = 2
		GPU id = 0 (NVIDIA GeForce RTX 3080)
		Layer-Device Extensions: count = 1
			VK_EXT_tooling_info : extension revision 1

		GPU id = 1 (NVIDIA GeForce RTX 3090 Ti)
		Layer-Device Extensions: count = 1
			VK_EXT_tooling_info : extension revision 1

0cc4m · 2024-03-19T12:44:02Z

@slaren Because Vulkan supports a lot more devices I haven't settled on a multigpu-default yet. Currently it just uses the first one by default, if you want more you need to set the GGML_VK_VISIBLE_DEVICES environment variable, similar to the cuda one. 0,1 for you.

We could use this chance to discuss and set a better default as well. I guess all dedicated GPUs would be sane?

slaren · 2024-03-19T13:39:10Z

Sorry, I had forgotten about GGML_VK_VISIBLE_DEVICES. This is the performance I get with the 3090 Ti. There was a big improvement in Q4_K performance with -ngl 0, and moderate improvements in most cases, although not as big as with the CUDA backend.

model	size	params	backend	ngl	n_batch	n_ubatch	test	t/s
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	0	1024	1024	pp 1024	221.47 ± 23.77
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	10	1024	1024	pp 1024	256.31 ± 5.29
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	20	1024	1024	pp 1024	389.19 ± 11.24
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	30	1024	1024	pp 1024	712.05 ± 72.24
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	99	1024	1024	pp 1024	972.84 ± 2.19
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	0	1024	1024	pp 1024	33.71 ± 4.48
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	10	1024	1024	pp 1024	274.82 ± 26.43
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	20	1024	1024	pp 1024	345.44 ± 4.25
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	30	1024	1024	pp 1024	574.53 ± 31.10
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	99	1024	1024	pp 1024	790.17 ± 0.47

build: d0d5de4 (2464)

model	size	params	backend	ngl	n_batch	n_ubatch	test	t/s
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	0	1024	1024	pp 1024	328.63 ± 8.48
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	10	1024	1024	pp 1024	354.88 ± 7.73
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	20	1024	1024	pp 1024	494.57 ± 33.10
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	30	1024	1024	pp 1024	797.17 ± 52.51
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan	99	1024	1024	pp 1024	983.79 ± 1.87
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	0	1024	1024	pp 1024	221.21 ± 7.87
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	10	1024	1024	pp 1024	282.71 ± 12.36
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	20	1024	1024	pp 1024	394.85 ± 20.59
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	30	1024	1024	pp 1024	682.70 ± 13.70
llama 7B Q4_K - Small	3.59 GiB	6.74 B	Vulkan	99	1024	1024	pp 1024	811.51 ± 0.51

build: 86386e2 (2460)

slaren · 2024-03-19T13:42:54Z

We could use this chance to discuss and set a better default as well. I guess all dedicated GPUs would be sane?

I would think so. It will still take a while, but in the future I would like to improve the device selection, allow the users to select the devices that they want to use by name, and improve the defaults. It is already possible to select what devices to use with -ts and -mg, but admittedly it is not very intuitive.

0cc4m · 2024-03-19T19:13:22Z

I found two issues that I have to fix before this can get merged:

The backend-offload code does not take APUs into account yet, so they suffer a big performance penalty with the current version
There's a validation issue that manifests as incoherence on Intel GPUs

I'll look into these problems in the next days.

MaggotHATE · 2024-03-19T19:42:48Z

Thanks for this update! Good news: restarting is fixed!
Bad news: RAM consumption is now higher than before, q6_k uses almost as much as q8 used to (tested on 7b).
VRAM use is unaffected, speeds are a bit better.
Win 8.1, 16GB DDR3, 1060 3GB, 7 layers only.

daniandtheweb · 2024-03-20T11:54:53Z

With this PR k quants performance on my card have almost reached and ,in q4_k_m, even surpassed ROCm's speed. Ram usage is slightly higher than on master and still higher than ROCm.
Great work!

Model: llama 2

AMD Radeon RX 5700 XT (RADV NAVI10)

model	size	params	backend	ngl	test	t/s	speedup
llama 7B Q4_0	3.56 GiB	6.74 B	ROCm	99	pp 512	325.12 ± 0.29
llama 7B Q4_0	3.56 GiB	6.74 B	ROCm	99	tg 128	60.25 ± 0.03
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan (master)	99	pp 512	320.24 ± 0.49
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan (master)	99	tg 128	37.73 ± 0.08
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan (PR)	99	pp 512	322.11 ± 0.43
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan (PR)	99	tg 128	38.02 ± 0.09
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	ROCm	99	pp 512	282.11 ± 0.28
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	ROCm	99	tg 128	45.72 ± 0.05
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	Vulkan (master)	99	pp 512	183.45 ± 0.36
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	Vulkan (master)	99	tg 128	50.65 ± 0.22
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	Vulkan (PR)	99	pp 512	282.72 ± 0.58	1.54
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	Vulkan (PR)	99	tg 128	51.34 ± 0.58
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	ROCm	99	pp 512	281.48 ± 0.30
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	ROCm	99	tg 128	43.92 ± 0.04
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	Vulkan (master)	99	pp 512	183.63 ± 1.09
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	Vulkan (master)	99	tg 128	37.10 ± 0.23
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	Vulkan (PR)	99	pp 512	269.24 ± 0.37	1.47
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	Vulkan (PR)	99	tg 128	37.51 ± 0.09

Nindaleth · 2024-03-20T22:05:21Z

Here are my numbers:
Radeon RX 6700 XT, Ryzen 5700X ECO, model mistral-7b-instruct-v0.2 fully offloaded to the GPU (unless specified otherwise). ROCm 6.0.

Only listing the quants where the pp result changed.
Vulkan0: AMD Radeon RX 6700 XT (RADV NAVI22) | uma: 0 | fp16: 1 | warp size: 64

model	size	params	backend	ngl	test	tk/s
llama 7B Q4_1	4.24 GiB	7.24 B	Vulkan (master)	99	pp 512	350.44 ± 0.60
llama 7B Q4_1	4.24 GiB	7.24 B	Vulkan (PR)	99	pp 512	415.73 ± 0.39
llama 7B Q5_0	4.65 GiB	7.24 B	Vulkan (master)	99	pp 512	348.05 ± 2.50
llama 7B Q5_0	4.65 GiB	7.24 B	Vulkan (PR)	99	pp 512	394.29 ± 0.45
llama 7B Q5_1	5.07 GiB	7.24 B	Vulkan (master)	99	pp 512	348.34 ± 0.73
llama 7B Q5_1	5.07 GiB	7.24 B	Vulkan (PR)	99	pp 512	400.94 ± 0.11
llama 7B Q2_K - Small	2.35 GiB	7.24 B	Vulkan (master)	99	pp 512	338.56 ± 0.71
llama 7B Q2_K - Small	2.35 GiB	7.24 B	Vulkan (PR)	99	pp 512	385.64 ± 0.33
llama 7B Q2_K - Medium	2.53 GiB	7.24 B	Vulkan (master)	99	pp 512	333.81 ± 1.93
llama 7B Q2_K - Medium	2.53 GiB	7.24 B	Vulkan (PR)	99	pp 512	368.42 ± 0.60
llama 7B IQ3_XS - 3.3 bpw	2.79 GiB	7.24 B	Vulkan (master)	99	pp 512	336.30 ± 2.64
llama 7B IQ3_XS - 3.3 bpw	2.79 GiB	7.24 B	Vulkan (PR)	99	pp 512	353.17 ± 0.80
llama 7B Q4_K - Small	3.86 GiB	7.24 B	Vulkan (master)	99	pp 512	345.65 ± 0.51
llama 7B Q4_K - Small	3.86 GiB	7.24 B	Vulkan (PR)	99	pp 512	356.07 ± 1.00
llama 7B Q4_K - Medium	4.07 GiB	7.24 B	Vulkan (master)	99	pp 512	345.98 ± 0.40
llama 7B Q4_K - Medium	4.07 GiB	7.24 B	Vulkan (PR)	99	pp 512	358.87 ± 0.45
llama 7B Q5_K - Medium	4.78 GiB	7.24 B	Vulkan (master)	99	pp 512	343.78 ± 2.35
llama 7B Q5_K - Medium	4.78 GiB	7.24 B	Vulkan (PR)	99	pp 512	350.55 ± 0.95
llama 7B Q6_K	5.53 GiB	7.24 B	Vulkan (master)	99	pp 512	327.14 ± 1.96
llama 7B Q6_K	5.53 GiB	7.24 B	Vulkan (PR)	99	pp 512	360.30 ± 1.30
llama 7B Q8_0	7.17 GiB	7.24 B	Vulkan (master)	99	pp 512	345.40 ± 0.61
llama 7B Q8_0	7.17 GiB	7.24 B	Vulkan (PR)	99	pp 512	405.50 ± 0.38

And a couple of tests with -ngl 0:

model	size	params	backend	test	tk/s
llama 7B Q4_K - Medium	4.07 GiB	7.24 B	Vulkan (master)	pp 512	108.21 ± 0.75
llama 7B Q4_K - Medium	4.07 GiB	7.24 B	Vulkan (PR)	pp 512	252.58 ± 2.44
llama 7B Q6_K	5.53 GiB	7.24 B	Vulkan (master)	pp 512	100.25 ± 0.56
llama 7B Q6_K	5.53 GiB	7.24 B	Vulkan (PR)	pp 512	231.54 ± 1.65
llama 7B Q8_0	7.17 GiB	7.24 B	Vulkan (master)	pp 512	94.35 ± 1.39
llama 7B Q8_0	7.17 GiB	7.24 B	Vulkan (PR)	pp 512	210.63 ± 1.53

Fix validation issue

0cc4m · 2024-03-23T12:23:16Z

@slaren Do you have an idea why test-backend-ops doesn't build with Vulkan support with cmake?

» build_vk/bin/test-backend-ops
Testing 1 backends

Backend 1/1 (CPU)
  Skipping CPU backend
1/1 backends passed
OK

It works fine with cublas.

slaren · 2024-03-23T12:25:18Z

Maybe ggml-backend.c is being built without GGML_USE_VULKAN defined, so it never gets added to the registry. Edit: or more likely, because ggml_backend_vk_reg_devices is being called before device_indices is populated.

0cc4m · 2024-03-23T12:36:19Z

Maybe ggml-backend.c is being built without GGML_USE_VULKAN defined, so it never gets added to the registry. Edit: or more likely, because ggml_backend_vk_reg_devices is being called before device_indices is populated.

Yeah, it was not populated yet. Thanks.

netrunnereve · 2024-03-24T20:41:03Z

Interestingly enough I'm actually seeing 25% slower prompt processing speed with this PR compared to master. This only happens on the K-quants and inference speed remains the same.

Maybe this is an architecture thing but it could also be due to the fact that I'm running in fp32 mode whereas all the other commenters have fp16 cards.

Vulkan0: AMD Radeon FirePro W8100 (RADV HAWAII) | uma: 0 | fp16: 0 | warp size: 64

model	size	params	backend	ngl	test	t/s
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan (master)	99	pp 512	93.89 ± 0.65
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan (PR)	99	pp 512	93.17 ± 0.37
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan (master)	99	tg 128	11.95 ± 0.02
llama 7B Q4_0	3.56 GiB	6.74 B	Vulkan (PR)	99	tg 128	11.92 ± 0.09
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	Vulkan (master)	99	pp 512	93.63 ± 0.17
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	Vulkan (PR)	99	pp 512	73.58 ± 0.27
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	Vulkan (master)	99	tg 128	8.79 ± 0.03
llama 7B Q4_K - Medium	3.80 GiB	6.74 B	Vulkan (PR)	99	tg 128	8.79 ± 0.08
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	Vulkan (master)	99	pp 512	93.08 ± 0.22
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	Vulkan (PR)	99	pp 512	69.47 ± 0.26
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	Vulkan (master)	99	tg 128	6.07 ± 0.05
llama 7B Q5_K - Medium	4.45 GiB	6.74 B	Vulkan (PR)	99	tg 128	5.97 ± 0.02
llama 7B Q6_K	5.15 GiB	6.74 B	Vulkan (master)	99	pp 512	98.62 ± 0.38
llama 7B Q6_K	5.15 GiB	6.74 B	Vulkan (PR)	99	pp 512	74.50 ± 0.27
llama 7B Q6_K	5.15 GiB	6.74 B	Vulkan (master)	99	tg 128	6.64 ± 0.02
llama 7B Q6_K	5.15 GiB	6.74 B	Vulkan (PR)	99	tg 128	6.67 ± 0.07
llama 7B Q8_0	6.67 GiB	6.74 B	Vulkan (master)	99	pp 512	100.23 ± 0.32
llama 7B Q8_0	6.67 GiB	6.74 B	Vulkan (PR)	99	pp 512	92.47 ± 0.31
llama 7B Q8_0	6.67 GiB	6.74 B	Vulkan (master)	99	tg 128	7.69 ± 0.03
llama 7B Q8_0	6.67 GiB	6.74 B	Vulkan (PR)	99	tg 128	7.67 ± 0.03

0cc4m · 2024-03-27T19:20:39Z

I fixed GET_ROWS and pulled upstream changes, seems to work. There might be an issue with f16, but that's not that important. I just have to fix UMA, then this PR can be merged.

0cc4m · 2024-03-27T19:26:41Z

Interestingly enough I'm actually seeing 25% slower prompt processing speed with this PR compared to master. This only happens on the K-quants and inference speed remains the same.

Maybe this is an architecture thing but it could also be due to the fact that I'm running in fp32 mode whereas all the other commenters have fp16 cards.

Thanks for testing this. I can confirm it's related to fp16 on AMD (GCN?) GPUs. I can only guess it's related to register pressure, since float32 uses twice the space for its variables. The k-quants need more registers to dequantize, and now that happens in the same shader as the matrix multiplication itself. I'll have to take a look in the future on whether I can mitigate that.

ggml.c

MaggotHATE · 2024-03-28T07:25:44Z

Since I reported previously on memory consumption, adding console log with the latest changes. At the moment both RAM and VRAM usage is significantly higher than with Clblast (which is missing backend and some operations) - around 400MB more VRAM and 4GB more RAM.

At the same time the main benefit of Vulkan now is higher prompt processing speed and more stable generation speed over time.

VK_log.txt

0cc4m · 2024-03-28T16:49:34Z

Since I reported previously on memory consumption, adding console log with the latest changes. At the moment both RAM and VRAM usage is significantly higher than with Clblast (which is missing backend and some operations) - around 400MB more VRAM and 4GB more RAM.

At the same time the main benefit of Vulkan now is higher prompt processing speed and more stable generation speed over time.

VK_log.txt

Thanks for the report, I don't pay enough attention to RAM use since my development server has 128GB. If Vulkan uses 4GB more RAM for the same number of offloaded layers it's probably some issue with the staging buffers. I'll take a look at some point.

0cc4m · 2024-03-29T14:05:24Z

I checked again and it seems that Vulkan UMA has not regressed. It works fine if you put no tensors or all tensors on GPU, but slows down if you do anything else. I should fix that in the future, but it is not necessary to hold back this PR for that reason, since master is in the same state.

I'll wait for the CI checks and merge this afterwards.

) * Fix Vulkan no kv offload incoherence * Add k-quant mul mat mat shaders * Rework working buffer allocation, reduces vram use noticeably Clean up cpu assist code, replaced with ggml-backend offload function * Default to all dedicated GPUs * Add fallback for integrated GPUs if no dedicated GPUs are found * Add debug info which device is allocating memory * Fix Intel dequant issue Fix validation issue * Fix Vulkan GGML_OP_GET_ROWS implementation * Clean up merge artifacts * Remove Vulkan warning

0cc4m added 4 commits March 9, 2024 07:41

Fix Vulkan no kv offload incoherence

492ad4b

Add k-quant mul mat mat shaders

cb6636e

Merge remote-tracking branch 'origin/master' into 0cc4m/vulkan-improv…

f315402

…ements

Rework working buffer allocation, reduces vram use noticeably

86386e2

Clean up cpu assist code, replaced with ggml-backend offload function

0cc4m requested a review from slaren March 19, 2024 09:44

0cc4m mentioned this pull request Mar 19, 2024

Multi GPU with Vulkan out of memory issue. #5848

Closed

slaren approved these changes Mar 19, 2024

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0cc4m added 2 commits March 19, 2024 19:50

Default to all dedicated GPUs

bcdd653

Add fallback for integrated GPUs if no dedicated GPUs are found

8ddd557

Add debug info which device is allocating memory

24e5039

Fix Intel dequant issue

1fceeb9

Fix validation issue

0cc4m mentioned this pull request Mar 22, 2024

llama : greatly reduce output buffer memory usage #6122

Merged

10 tasks

0cc4m added 2 commits March 27, 2024 19:54

Fix Vulkan GGML_OP_GET_ROWS implementation

d00b11b

Merge upstream changes, fix conflicts

6cb07fb

slaren reviewed Mar 27, 2024

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Clean up merge artifacts

0cda567

compilade mentioned this pull request Mar 29, 2024

Vulkan evaluation failed: ggml_vulkan: Error: Missing op: GET_ROWS #6376

Closed

Remove Vulkan warning

b7863ab

0cc4m merged commit ba0c7c7 into master Mar 29, 2024

0cc4m deleted the 0cc4m/vulkan-improvements branch March 29, 2024 16:29

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Vulkan k-quant mmq and ggml-backend offload functionality #6155

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Vulkan k-quant mmq and ggml-backend offload functionality #6155

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