ik_llama.cpp/github-data/pull_requests/332 - Better TG performance for GQA models _CPU_.md

🔀 #332 - Better TG performance for GQA models (CPU)

Author	ikawrakow
State	❌ Closed
Created	2025-04-16
Updated	2025-04-17

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Description

This PR adds improved TG performance on the CPU for GQA models (LLaMA-2+, Gemma, etc.). We see performance gains with and without FA. The gains without FA are fairly minor and come from a different way of distributing the work between the threads for the K*Q and V*softmax(K*Q) matrix multiplications. The performance gains with FA enabled are very significant, and FA now outperforms no-FA also for TG.

Here is an example for LLaMA-3.1-8B-Instruct. Model is quantized with Q4_0, KV cache is Q8_0 (V-cache is f16 when FA is not enabled). Results are for a R$yzen-5975WX CPU (vanilla AVX2). Also included for comparison are mainline llama.cpp results (build 5139) with FA enabled shown with orange symbols. Results are obtained with llama-sweet-bench using

./bin/llama-sweep-bench -m $model -c 10240 -ctk q8_0 -ctv q8_0 -t 32 -fa

The x-axis is N_KV, the number of tokens in the KV cache.

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💬 Conversation

👤 ikawrakow commented the 2025-04-16 at 16:04:38:

Here another comparison to mainline, this time for Gemma3-12B-Instruct. Only runs with FA enabled, Q8_0 KV-cache, Q4_0 quantized model, Risen-5975WX CPU. I have rerun the mainline benchmark multiple times, dropping caches or not between runs, and the peculiar sudden drop in performance for the first 1024 tokens in the KV cache remained unchanged. Here mainline does significantly better relative to ik_llama.cpp compared to LLaMA-3.1-8B in the above graph. I suspect this is due to the fact that the benefit from the improvement this PR adds is less. Gemma3 has 16 attention heads in total and 8 KV heads. This results in the K*Q and V*softmax(K*Q) GEMM's for TG to be done with matrices with just 2 rows (compared to 4 rows for LLaMA-3), so the gain from using GEMM instead of GEMV is less. It is also possible that there is something in mainline that makes it perform better with the Gemma3 head size of 256 (vs 128 for LLaMA-3). The mainline CPU code has changed a lot since I left the project, so I cannot say I know very well what happens there.

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👤 saood06 commented the 2025-04-17 at 00:32:59:

and FA now outperforms no-FA also for TG.

Nice.

Results are obtained with llama-sweet-bench using

./bin/llama-sweep-bench -m $model -c 10240 -ctk q8_0 -ctv q8_0 -t 32 -fa

Do you still have the raw markdown results? I know PP wasn't affected by this PR but I'm curious where it stands vs mainline.

Here mainline does significantly better relative to ik_llama.cpp compared to LLaMA-3.1-8B in the above graph.

I wonder if they cross over at higher contexts the gap does seem to be closing here.

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👤 ikawrakow commented the 2025-04-17 at 05:54:21:

Do you still have the raw markdown results? I know PP wasn't affected by this PR but I'm curious where it stands vs mainline.

Mainline PP performance with FA is embarrassing. I also picked the fastest mainline quant that receives an extraordinary amount of attention (Q4_0). I had not kept the logs, so reran sweep-bench this morning up to a context of 16k. This particular computer is quite sensitive to dropping caches between runs. It seems also that results are somewhat sensitive to the amount of KV cache allocated, so slightly different from yesterday.

Gemma3-12B-Instruct

At 16k tokens mainline TG performance is indeed slightly better than ik_llama.cpp. But mainline PP performance drops from 55.5% at zero context to 42.4% at 16k tokens.

• Mainline

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	4.669	109.67	12.164	10.52
512	128	512	4.811	106.42	13.061	9.80
512	128	1024	5.049	101.40	13.818	9.26
512	128	1536	5.164	99.15	13.960	9.17
512	128	2048	5.280	96.97	14.107	9.07
512	128	2560	5.423	94.40	14.248	8.98
512	128	3072	5.619	91.11	14.395	8.89
512	128	3584	5.823	87.92	14.535	8.81
512	128	4096	6.070	84.35	14.677	8.72
512	128	4608	6.306	81.19	14.825	8.63
512	128	5120	6.547	78.20	14.969	8.55
512	128	5632	6.890	74.31	15.131	8.46
512	128	6144	7.227	70.85	15.281	8.38
512	128	6656	7.513	68.15	15.394	8.32
512	128	7168	7.918	64.67	15.537	8.24
512	128	7680	8.334	61.43	15.680	8.16
512	128	8192	8.800	58.18	15.830	8.09
512	128	8704	9.200	55.65	15.971	8.01
512	128	9216	9.523	53.76	16.101	7.95
512	128	9728	10.048	50.95	16.242	7.88
512	128	10240	10.495	48.78	16.371	7.82
512	128	10752	10.955	46.73	16.507	7.75
512	128	11264	11.375	45.01	16.662	7.68
512	128	11776	11.837	43.26	16.798	7.62
512	128	12288	12.320	41.56	16.949	7.55
512	128	12800	12.613	40.59	17.085	7.49
512	128	13312	12.815	39.95	17.208	7.44
512	128	13824	13.100	39.08	17.364	7.37
512	128	14336	13.466	38.02	17.518	7.31
512	128	14848	13.669	37.46	17.655	7.25
512	128	15360	13.789	37.13	17.797	7.19
512	128	15872	13.874	36.90	17.937	7.14

• ik_llama.cpp

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	2.593	197.46	12.301	10.41
512	128	512	2.662	192.34	12.501	10.24
512	128	1024	2.756	185.77	12.703	10.08
512	128	1536	2.854	179.42	12.946	9.89
512	128	2048	2.946	173.78	13.143	9.74
512	128	2560	3.040	168.42	13.331	9.60
512	128	3072	3.136	163.26	13.507	9.48
512	128	3584	3.235	158.25	13.711	9.34
512	128	4096	3.336	153.48	13.907	9.20
512	128	4608	3.432	149.20	14.088	9.09
512	128	5120	3.530	145.05	14.290	8.96
512	128	5632	3.632	140.99	14.483	8.84
512	128	6144	3.729	137.31	14.673	8.72
512	128	6656	3.834	133.53	14.879	8.60
512	128	7168	3.934	130.14	15.074	8.49
512	128	7680	4.046	126.55	15.266	8.38
512	128	8192	4.140	123.67	15.443	8.29
512	128	8704	4.243	120.66	15.616	8.20
512	128	9216	4.342	117.91	15.838	8.08
512	128	9728	4.450	115.06	16.008	8.00
512	128	10240	4.552	112.48	16.197	7.90
512	128	10752	4.721	108.46	16.429	7.79
512	128	11264	4.762	107.51	16.622	7.70
512	128	11776	4.869	105.16	16.823	7.61
512	128	12288	4.973	102.96	16.982	7.54
512	128	12800	5.077	100.84	17.208	7.44
512	128	13312	5.175	98.93	17.419	7.35
512	128	13824	5.278	97.02	17.603	7.27
512	128	14336	5.461	93.75	17.798	7.19
512	128	14848	5.560	92.08	19.126	7.12
512	128	15360	5.717	89.55	19.383	7.06
512	128	15872	5.891	86.91	19.640	7.00

LLaMA-3.1-8B-Instruct

Here mainline does not do well for PP or TG. Mainline TG is 55.5% of ik_llama.cpp at 16k tokens. Mainline PP is totally embarrassing. It starts at about 60% of ik_llama.cpp for zero context, and finishes at 7.2% at 16k (14X slower). So, whatever was done to optimize performance for a head size of 256, it is a killer for a head size of 128 (the most common head size). Here the data:

• Mainline

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	2.737	187.04	7.548	16.96
512	128	512	3.185	160.76	7.953	16.09
512	128	1024	3.721	137.60	8.409	15.22
512	128	1536	4.219	121.35	8.826	14.50
512	128	2048	4.711	108.68	9.199	13.91
512	128	2560	5.206	98.34	9.592	13.34
512	128	3072	5.704	89.76	9.980	12.83
512	128	3584	6.252	81.89	10.370	12.34
512	128	4096	6.867	74.55	10.765	11.89
512	128	4608	7.507	68.20	11.157	11.47
512	128	5120	8.231	62.21	11.552	11.08
512	128	5632	9.214	55.57	11.941	10.72
512	128	6144	10.467	48.91	12.330	10.38
512	128	6656	11.646	43.96	12.713	10.07
512	128	7168	13.104	39.07	13.109	9.76
512	128	7680	14.813	34.56	13.500	9.48
512	128	8192	16.570	30.90	13.885	9.22
512	128	8704	18.246	28.06	14.277	8.97
512	128	9216	20.142	25.42	14.675	8.72
512	128	9728	21.729	23.56	15.072	8.49
512	128	10240	23.615	21.68	15.454	8.28
512	128	10752	25.406	20.15	15.840	8.08
512	128	11264	27.299	18.76	16.236	7.88
512	128	11776	29.122	17.58	16.625	7.70
512	128	12288	31.079	16.47	17.012	7.52
512	128	12800	33.052	15.49	17.407	7.35
512	128	13312	34.958	14.65	17.796	7.19
512	128	13824	37.170	13.77	18.188	7.04
512	128	14336	39.425	12.99	18.570	6.89
512	128	14848	41.661	12.29	18.959	6.75
512	128	15360	43.766	11.70	19.350	6.62
512	128	15872	46.129	11.10	19.730	6.49

• ik_llama.cpp

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
512	128	0	1.638	312.56	7.739	16.54
512	128	512	1.661	308.28	7.852	16.30
512	128	1024	1.705	300.35	7.961	16.08
512	128	1536	1.766	289.90	8.075	15.85
512	128	2048	1.806	283.52	8.170	15.67
512	128	2560	1.860	275.34	8.261	15.50
512	128	3072	1.914	267.51	8.363	15.31
512	128	3584	1.981	258.45	8.468	15.11
512	128	4096	2.022	253.22	8.592	14.90
512	128	4608	2.076	246.61	8.706	14.70
512	128	5120	2.132	240.12	8.800	14.55
512	128	5632	2.189	233.92	8.902	14.38
512	128	6144	2.240	228.58	8.998	14.23
512	128	6656	2.298	222.81	9.093	14.08
512	128	7168	2.352	217.66	9.191	13.93
512	128	7680	2.407	212.69	9.297	13.77
512	128	8192	2.462	207.92	9.409	13.60
512	128	8704	2.519	203.22	9.514	13.45
512	128	9216	2.573	199.02	9.619	13.31
512	128	9728	2.630	194.71	9.702	13.19
512	128	10240	2.683	190.82	9.796	13.07
512	128	10752	2.739	186.91	9.904	12.92
512	128	11264	2.795	183.19	10.018	12.78
512	128	11776	2.851	179.62	10.124	12.64
512	128	12288	2.905	176.24	10.228	12.51
512	128	12800	2.963	172.78	10.321	12.40
512	128	13312	3.018	169.64	10.413	12.29
512	128	13824	3.078	166.34	10.538	12.15
512	128	14336	3.133	163.43	10.632	12.04
512	128	14848	3.192	160.40	10.738	11.92
512	128	15360	3.249	157.61	10.838	11.81
512	128	15872	3.305	154.91	10.942	11.70

Btw, my surprise at the 6X drop in PP performance for DeepSeek-V3/R1 that I expressed elsewhere was based on results such as these. ik_llama.cpp PP performance at 16k tokens is 2X lower for LLaMA-3.1, and 2.3X lower for Gemma3.

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👤 saood06 commented the 2025-04-17 at 07:45:00:

Mainline PP performance with FA is embarrassing.

It is really nice being able to use FA here and benefit.

I also picked the fastest mainline quant that receives an extraordinary amount of attention (Q4_0).

For gemma this also makes the most sense as they released QAT versions of Q4_0 (this being the best version for 12B, some measurements here).

I had not kept the logs, so reran sweep-bench this morning up to a context of 16k.

Thanks for doing that.

It seems also that results are somewhat sensitive to the amount of KV cache allocated, so slightly different from yesterday.

Ya surprisingly the newer run with higher KV performed better looking at both.

At 16k tokens mainline TG performance is indeed slightly better than ik_llama.cpp.

Here's the visual generated with the python script in the sweep-bench example folder, in order to see the crossover point.

But mainline PP performance drops from 55.5% at zero context to 42.4% at 16k tokens.

Yes both model the PP graphs just show ik_llama clearly above mainline.

LLaMA-3.1-8B-Instruct

Here mainline does not do well for PP or TG. Mainline TG is 55.5% of ik_llama.cpp at 16k tokens. Mainline PP is totally embarrassing. It starts at about 60% of ik_llama.cpp for zero context, and finishes at 7.2% at 16k (14X slower). So, whatever was done to optimize performance for a head size of 256, it is a killer for a head size of 128 (the most common head size). Here the data:

PP graph again not very interesting but TG is interesting showing the different curves.

Btw, my surprise at the 6X drop in PP performance for DeepSeek-V3/R1 that I expressed elsewhere was based on results such as these. ik_llama.cpp PP performance at 16k tokens is 2X lower for LLaMA-3.1, and 2.3X lower for Gemma3.

Ya that architecture's performance surprises me too like when I saw peak batched TG performance for Deepseek being higher than PP performance instead of just approaching it like I normally observe.