ik_llama.cpp/github-data/discussions/491--rtr actually hurts prompt t_s for large ubatch_.md at b685f9b4aafca252dd99ea011ffab65dfb7ad143

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🗣️ #491 - -rtr actually hurts prompt t/s for large ubatch?

Author	`Ph0rk0z`
Created	2025-06-03
Updated	2025-06-11

Description

I had long assumed that -RTR was a universal speedup and just like repacking, it would help your performance always. Seems that is not the case.

Qwen 235b command line

    CUDA_VISIBLE_DEVICES=0,1,2,3 numactl --interleave=all ./bin/llama-sweep-bench \
    -m Smoothie-Qwen3-235B-A22B.IQ4_XS.gguf \
    -t 48 \
    -c 32768 \
    --numa distribute \
    -ngl 95 \
    -ctk q8_0 \
    -ctv q8_0 \
    -fa \
    -fmoe \
    -amb 64 \
    -b 4096 \
    -ub 4096 \
    -ot "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|)\.ffn_.*_exps.=CUDA0" \
    -ot "blk\.(14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29)\.ffn_.*_exps.=CUDA1" \
    -ot "blk\.(30|31|32|33|34|35|36|37|38|39|40|41|42|43|44|45|)\.ffn_.*_exps.=CUDA2" \
    -ot "blk\.(46|47|48|49|50|51|52|53|54|55|56|57|58|59)\.ffn_.*_exps.=CUDA3" \
    -ot "\.ffn_.*_exps.=CPU"

No RTR Buffers

llama_kv_cache_init:      CUDA0 KV buffer size =   816.01 MiB
llama_kv_cache_init:      CUDA1 KV buffer size =   816.01 MiB
llama_kv_cache_init:      CUDA2 KV buffer size =   816.01 MiB
llama_kv_cache_init:      CUDA3 KV buffer size =   748.01 MiB
llama_new_context_with_model: KV self size  = 3196.00 MiB, K (q8_0): 1598.00 MiB, V (q8_0): 1598.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.58 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=1)
llama_new_context_with_model:      CUDA0 compute buffer size =  1856.02 MiB
llama_new_context_with_model:      CUDA1 compute buffer size =  1094.02 MiB
llama_new_context_with_model:      CUDA2 compute buffer size =   836.00 MiB
llama_new_context_with_model:      CUDA3 compute buffer size =  2502.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =   576.05 MiB
llama_new_context_with_model: graph nodes  = 3672
llama_new_context_with_model: graph splits = 183
main: n_kv_max = 32768, n_batch = 4096, n_ubatch = 4096, flash_attn = 1, n_gpu_layers = 95, n_threads = 48, n_threads_batch = 48

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	14.283	286.78	65.942	15.53
4096	1024	4096	14.803	276.70	68.941	14.85
4096	1024	8192	15.461	264.92	73.586	13.92
4096	1024	12288	15.831	258.74	77.875	13.15
4096	1024	16384	16.185	253.08	81.513	12.56
4096	1024	20480	16.926	241.99	85.266	12.01

Buffers with RTR

llama_kv_cache_init:      CUDA0 KV buffer size =   816.01 MiB
llama_kv_cache_init:      CUDA1 KV buffer size =   816.01 MiB
llama_kv_cache_init:      CUDA2 KV buffer size =   816.01 MiB
llama_kv_cache_init:      CUDA3 KV buffer size =   748.01 MiB
llama_new_context_with_model: KV self size  = 3196.00 MiB, K (q8_0): 1598.00 MiB, V (q8_0): 1598.00 MiB
llama_new_context_with_model:  CUDA_Host  output buffer size =     0.58 MiB
llama_new_context_with_model: pipeline parallelism enabled (n_copies=1)
llama_new_context_with_model:      CUDA0 compute buffer size =  1664.02 MiB
llama_new_context_with_model:      CUDA1 compute buffer size =  1094.02 MiB
llama_new_context_with_model:      CUDA2 compute buffer size =  1024.02 MiB
llama_new_context_with_model:      CUDA3 compute buffer size =  2502.00 MiB
llama_new_context_with_model:  CUDA_Host compute buffer size =  1024.05 MiB
llama_new_context_with_model: graph nodes  = 3672
llama_new_context_with_model: graph splits = 149

main: n_kv_max = 32768, n_batch = 4096, n_ubatch = 4096, flash_attn = 1, n_gpu_layers = 95, n_threads = 48, n_threads_batch = 48

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	24.221	169.11	59.405	17.24
4096	1024	4096	24.852	164.82	62.359	16.42
4096	1024	8192	25.570	160.19	67.178	15.24
4096	1024	12288	26.293	155.78	71.996	14.22
4096	1024	16384	26.979	151.82	76.468	13.39

It's even worse on deepseek where my prompt speeds were cut in half while losing about 1.5t/s of TG only. Another thing of note is that no repacking causes much more large transfers to the GPU. I saw rates of up to 16GBs going between cards and I assume the system?

Peculiar thing though, for smaller batches:

235b ub 1024

CUDA_VISIBLE_DEVICES=0,1,2,3 numactl --interleave=all ./bin/llama-sweep-bench \
    -m Smoothie-Qwen3-235B-A22B.IQ4_XS.gguf \
    -t 48 \
    -c 32768 \
    --numa distribute \
    -ngl 95 \
    -ctk q8_0 \
    -ctv q8_0 \
    -fa \
    -rtr \
    -fmoe \
    -amb 512 \
    -ub 1024 \
    -ot "blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15)\.ffn_.*_exps.=CUDA0" \
    -ot "blk\.(16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32)\.ffn_.*_exps.=CUDA1" \
    -ot "blk\.(33|34|35|36|37|38|39|40|41|42|43|44|45|46|47|48|49)\.ffn_.*_exps.=CUDA2" \
    -ot "blk\.(50|51|52|53|54|55|56|57|58|59|60|61|62|63|64|65|66)\.ffn_.*_exps.=CUDA3" \
    -ot "\.ffn_.*_exps.=CPU"

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
1024	256	0	5.432	188.50	13.878	18.45
1024	256	1024	5.402	189.55	14.069	18.20
1024	256	2048	5.434	188.43	14.268	17.94
1024	256	3072	5.514	185.71	14.499	17.66
1024	256	4096	5.543	184.74	14.655	17.47
1024	256	5120	5.566	183.96	15.034	17.03
1024	256	6144	5.624	182.08	15.241	16.80
1024	256	7168	5.700	179.64	15.547	16.47
1024	256	8192	5.732	178.66	15.836	16.17
1024	256	9216	5.820	175.96	16.136	15.87
1024	256	10240	5.812	176.18	16.415	15.60
1024	256	11264	5.888	173.92	16.751	15.28
1024	256	12288	5.907	173.37	16.951	15.10
1024	256	13312	5.994	170.84	17.151	14.93
1024	256	14336	5.998	170.72	17.394	14.72
1024	256	15360	6.043	169.46	17.623	14.53
1024	256	16384	6.139	166.80	17.983	14.24

Without -rtr, this makes ~120 prompt at most. Anyone know the why or noticed something similar?

🗣️ Discussion

👤 Ph0rk0z replied the 2025-06-04 at 15:59:57:

I played around with offline repacking next. Oh boy.

Offline repacking on 4096 batch.

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	24.349	168.22	69.065	14.83
4096	1024	4096	24.815	165.06	71.880	14.25
4096	1024	8192	25.604	159.97	76.457	13.39
4096	1024	12288	26.288	155.81	80.361	12.74

It seems like performance here is identical to using -rtr. Debuff to text generation likely from mmap.

Ok.. so let's try it in a configuration where repacking previously helped like the last one in the previous post. Only 6 layers are incorrectly packed and everything has gone into the toilet.

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
1024	256	0	6.992	146.46	192.370	1.33
1024	256	1024	6.969	146.95	192.509	1.33

Then I indiscriminately repacked the whole model to see what would happen. It got just as bad. Lots of transfers.Could be related to offload policy? I didn't even bother waiting for the first iteration it took so long. CPU running at 10 cores from the 1000% usage.

And finally I packed the model correctly AND used the configuration that produced a speed gain.

with mmap

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
1024	256	0	6.306	162.40	15.561	16.45
1024	256	1024	5.993	170.87	15.743	16.26
1024	256	2048	6.004	170.54	15.897	16.10
1024	256	3072	5.882	174.10	16.071	15.93
1024	256	4096	6.295	162.67	16.253	15.75
1024	256	5120	6.144	166.67	16.608	15.41
1024	256	6144	6.143	166.70	16.833	15.21
1024	256	7168	6.280	163.07	17.086	14.98
1024	256	8192	6.298	162.58	17.373	14.74

no mmap

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
1024	256	0	5.759	177.82	14.442	17.73
1024	256	1024	5.639	181.59	14.523	17.63
1024	256	2048	5.867	174.53	14.656	17.47
1024	256	3072	5.900	173.56	14.833	17.26
1024	256	4096	6.026	169.92	15.031	17.03
1024	256	5120	6.069	168.73	15.389	16.63
1024	256	6144	5.849	175.07	15.564	16.45
1024	256	7168	5.943	172.31	15.939	16.06
1024	256	8192	6.154	166.39	16.184	15.82

Does it help to cache the model first? Let's run with mmap again....

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
1024	256	0	6.441	158.99	15.466	16.55
1024	256	1024	6.111	167.56	15.717	16.29
1024	256	2048	5.875	174.30	15.810	16.19
1024	256	3072	6.029	169.84	16.001	16.00
1024	256	4096	6.150	166.52	16.170	15.83
1024	256	5120	6.010	170.39	16.537	15.48
1024	256	6144	6.008	170.44	16.727	15.30
1024	256	7168	6.332	161.73	17.038	15.02
1024	256	8192	6.277	163.13	17.328	14.77

NOPE!

So the point to the whole story, if anyone cares, is that even a few mis-packed layers will tank your speeds. Feels like there is no point to posting R4/R8 quants because the user will have to repack them anyway unless using the EXACT configuration of the author. What am I missing here?

As a bonus.. let's find where RTR starts to help prompt processing...

First I'll take a new baseline because it seems textgen is not working so good after packing/loading/etc. Could be I need to drop caches?

4096 no rtr/no-mmap Baseline

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	14.588	280.78	71.871	14.25
4096	1024	4096	14.877	275.33	74.257	13.79
4096	1024	8192	15.500	264.25	78.862	12.98
4096	1024	12288	15.919	257.30	83.039	12.33
4096	1024	16384	16.476	248.60	87.030	11.77

That's the highest we will get for now.

2048 without RTR with no-mmap

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
2048	512	0	11.606	176.47	35.719	14.33
2048	512	2048	11.586	176.77	36.388	14.07
2048	512	4096	11.683	175.30	37.146	13.78
2048	512	6144	11.813	173.37	38.241	13.39
2048	512	8192	11.950	171.38	39.246	13.05
2048	512	10240	12.194	167.95	40.579	12.62
2048	512	12288	12.208	167.75	41.348	12.38
2048	512	14336	12.412	165.00	42.410	12.07
2048	512	16384	12.407	165.07	43.277	11.83

2048 with rtr

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
2048	512	0	13.308	153.89	32.755	15.63
2048	512	2048	13.167	155.54	33.466	15.30
2048	512	4096	13.308	153.89	34.117	15.01
2048	512	6144	13.351	153.40	35.396	14.47
2048	512	8192	13.539	151.27	36.420	14.06
2048	512	10240	14.000	146.28	37.873	13.52
2048	512	12288	14.011	146.17	38.719	13.22
2048	512	14336	14.113	145.11	39.612	12.93
2048	512	16384	14.596	140.32	40.743	12.57

So still a debuff to prompt processing and a mild gain to t/g

Let's try something else....

2048/1024 -rtr

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
1024	256	0	6.837	149.78	16.543	15.47
1024	256	1024	6.830	149.93	16.713	15.32
1024	256	2048	6.885	148.73	16.821	15.22
1024	256	3072	7.085	144.54	17.057	15.01
1024	256	4096	6.899	148.42	17.248	14.84
1024	256	5120	7.106	144.10	17.608	14.54
1024	256	6144	6.760	151.47	17.794	14.39
1024	256	7168	7.181	142.60	18.080	14.16
1024	256	8192	7.154	143.13	18.325	13.97

2048/1024 -no rtr and no-mmap

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
1024	256	0	9.905	103.38	17.792	14.39
1024	256	1024	9.711	105.45	17.938	14.27
1024	256	2048	9.793	104.56	18.090	14.15
1024	256	3072	9.786	104.64	18.292	14.00
1024	256	4096	9.824	104.24	18.465	13.86
1024	256	5120	9.854	103.92	18.844	13.59
1024	256	6144	9.874	103.71	19.033	13.45
1024	256	7168	9.930	103.12	19.309	13.26
1024	256	8192	10.060	101.79	19.568	13.08

Ok.. now prompt processing finally fell.. the original observed effect.

So then -rtr or repacking is only useful in the case of ub being half the batch size? It does allow you to generate text a little bit faster in every test at least.

👤 ikawrakow replied the 2025-06-04 at 16:48:34:

Perhaps to understand how repacked quants behave on the CPU and CUDA, it is easier to take a smaller model that would completely fit one GPU, quantize with with --pure to your favorite quant and corresponding repacked variant, and then

Run fully offloaded to the GPU
Run CPU-only

It is an easy exercise, does not require an imatrix as you are not after the best possible quantization quality, and if you pick a model that is not too large, it is very quick to do.

Without having understood what the repacking does or does not do for you, it becomes very hard to sort out the big models with partial offloads, offload policy, numa, what runs on the GPU or CPU when and why, etc.

👤 Ph0rk0z replied the 2025-06-04 at 17:17:17:
Worth a try. I will have to. I'm repacking exactly what I don't put on GPU and watching the layers in quantize, i.e which become _R8. One other metric would be to do 4096/2048 and see if it really is correlated to half batch size or bound to the 1024 size.

Is there a way to print exactly what tensors are repacked by RTR? I could be missing some tiny layers it did on it's own by using the regex offline.

Textgen is back to 18.x t/s after I dropped caches but prompt processing benchmarks hold universally through my tests.

👤 Ph0rk0z replied the 2025-06-05 at 11:48:40:
So I got it to print the tensors. The one that gets repacked by RTR and not offline repacking is token_embd. I had issues moving that tensor to either CPU or GPU manually.

Also notice that quantize will repack to R8, is there a difference between that and R4 as far as the various cuda implementations you are adding?

👤 ikawrakow replied the 2025-06-05 at 11:56:57:
token_embd.weight is never repacked and always stays on the CPU. It should not go to the GPU, and it should not get repacked. If you managed to make it repack, that's a bug, and you should tell me how you did it.

For some quantization one gets better CPU performance by interleaving 8 rows, so these are the _R8 quants. Q4_0, Q8_0 and IQ4_XS get repacked to _R8, all others are _R4. Some of those that are _R4 would benefit from being _R8, but I haven't done it, and now that there are _R4 quantized models floating around the Internet, I don't want to break backwards compatibility (and I don't want to carry _R4 and _R8 version of the same quantization type), so it will stay like this.

👤 Ph0rk0z replied the 2025-06-05 at 12:49:05:
I uncommented your line near where it says REPACKED XX Tensors which purportedly printed what was repacked. Everything else matches what I sent to CPU. Either the print is incorrect or it repacked it.

Its strange too because I had tried to find layers to to throw on the CPU for just a few MB since my command line was OOM at 22k. Finally settled on 10 ffn_gate_inp towards the end. When I put token_embd=CPU I'd get a crash on qwen right away.

I just realized that all of my quants are IQ something. Wonder if it's related. Also tried offload policy from -1 to 29, negligible speed differences all around. Got deepseek lite a while ago which fits on one GPU but it's also IQ4_XS. Perhaps I should download a Q4_K instead.

edit:I enabled a further debug printout that says what got repacked to what and emb isn't there.

👤 Ph0rk0z replied the 2025-06-06 at 17:29:36:

Finally got around to testing a smaller model. Non IQ quant as well.

DeepSeek-V2-Lite-Chat.i1-Q4_K_M

CUDA_VISIBLE_DEVICES= numactl --interleave=all ./bin/llama-sweep-bench \
-m DeepSeek-V2-Lite-Chat.i1-Q4_K_M.gguf \
-t 48 \
-c 32768 \
--numa distribute \
-ngl 0 \
-ctk q8_0 \
-ctv q8_0 \
-fa \
-fmoe \
-rtr \
-b 4096 \
-ub 4096

No RTR 48c CPU distribute, cache on GPU

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	2.955	1386.18	36.494	28.06
4096	1024	4096	3.047	1344.07	60.110	17.04
4096	1024	8192	3.338	1227.20	82.831	12.36
4096	1024	12288	3.611	1134.32	103.469	9.90
4096	1024	16384	3.861	1060.81	125.330	8.17

RTR 48c CPU distribute, Cache on GPU (iqk_repack_tensor(output.weight): q6_K -> q6_k_r4. 102400 rows, 3200 chunks, 48 threads)

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	11.081	369.65	32.316	31.69
4096	1024	4096	13.410	305.44	53.593	19.11
4096	1024	8192	15.889	257.79	74.674	13.71

24 cores, numa isolate + RTR + no interleave

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	19.223	213.08	30.327	33.76
4096	1024	4096	23.378	175.21	64.052	15.99
4096	1024	8192	28.008	146.25	97.014	10.56

24 cores, no interleave + no rtr + numa isolate

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	3.352	1221.83	46.758	21.90
4096	1024	4096	3.448	1187.76	81.010	12.64
4096	1024	8192	3.730	1098.15	113.951	8.99

GPU Fully

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	0.730	5613.13	7.402	138.33
4096	1024	4096	0.863	4745.09	10.398	98.48
4096	1024	8192	1.115	3674.86	13.378	76.55

No GPU full cores no rtr

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	13.485	303.75	36.449	28.09
4096	1024	4096	15.527	263.81	58.686	17.45
4096	1024	8192	18.000	227.55	79.114	12.94

No GPU full cores RTR

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	10.863	377.07	33.246	30.80
4096	1024	4096	13.005	314.95	54.394	18.83
4096	1024	8192	15.463	264.88	75.656	13.53

It looks like on this system, RTR only helps when there is no GPU involved or the ubatch is 1024 (previous tests). In every other case, RTR lowers the prompt processing by a lot but improves TG.

👤 ciprianveg replied the 2025-06-10 at 16:08:25:
I noticed it too, and iQ3_XXS_UD pp speed is affected by rtr much more than other quants, it drops from 250t/s to 26t/s, cca 10x slower. q2_xl_ud drops only from 245 to 140t/s. I am using no-mmap and swap disabled..

It is a pitty because while dropping pp speed 90%, it increases the generation speed by 40%.

i have a TR 3955 and 2x3090. built with: cmake -B build -DGGML_CUDA=ON -DGGML_RPC=OFF -DGGML_BLAS=OFF -DGGML_SCHED_MAX_COPIES=1 -DGGML_CUDA_IQK_FORCE_BF16=1

started with: -ctx-size 71680
-ctk q8_0
-mla 3
-fa
-amb 512
-fmoe
--temp 0.6
--top_p 0.95
--min_p 0.01
--n-gpu-layers 63
-ot "blk.[0-3].ffn_up_exps=CUDA0,blk.[0-3].ffn_gate_exps=CUDA0,blk.[0-3].ffn_down_exps=CUDA0"
-ot "blk.1[0-1].ffn_up_exps=CUDA1,blk.1[0-1].ffn_gate_exps=CUDA1,blk.1[0].ffn_down_exps=CUDA1"
--override-tensor exps=CPU
--parallel 1
--threads 16
--threads-batch 15
--host 0.0.0.0 --port 5002
--ubatch-size 7168 --batch-size 7168 --no-mmap
BUT, if i build it with: cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DGGML_CUDA=ON -DGGML_SCHED_MAX_COPIES=1

no pp decrease anymore, but no tg speed increase, too..
👤 Ph0rk0z replied the 2025-06-11 at 11:40:47:
Could it be using BLAS instead of cuda when built with it? While ubatch size 1024 isn't as good as 4096+, it gives me a happy medium to use the RTR's textgen speed increase.

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🗣️ #491 - -rtr actually hurts prompt t/s for large ubatch?

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