Bug: PP Perf Regression after CUDA Graphs PR#689

[usrlocalben]

What happened?

PP Perf decrease of ~25% following the CUDA Graphs PR #689 .

I've been suspicious for a while by seat-of-the-pants impression, but didn't take time to measure until now.

Hardware is 2S EPYC 9115 w/RTX 8000 (Turing)

Tested with CUDA 12.6 and 13.0

aside: I don't observe any problems using CUDA 13.0

aside2: It seems unfortunate that there is so much refactoring, the gpt-oss addition, and whitespace changes mixed in with the graphs PR, it's at least difficult for an outsider to read and get a sense of which concept could be the cause.

-b 4096 -ub 4096
-mla 2 -fa -fmoe
-ngl 999 -ot exps=CPU
-m Kimi-K2-0905-DQ4_K.gguf

quant is my own, but the same config as anikifoss' K2.

-DGGML_CUDA_USE_GRAPHS=OFF in the build invocation was only added in the CUDA Graphs commit, normally I build without this line regardless of ON/OFF value.

Commit prior to CUDA Graphs (e082df4)

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	44.583	91.87	93.665	10.93
4096	1024	4096	46.379	88.32	98.202	10.43
4096	1024	8192	48.673	84.15	104.752	9.78
4096	1024	12288	51.006	80.30	111.377	9.19
4096	1024	16384	53.209	76.98	119.035	8.60
4096	1024	20480	55.804	73.40	130.798	7.83
4096	1024	24576	57.336	71.44	137.132	7.47
4096	1024	28672	59.623	68.70	144.862	7.07

CUDA_GRAPHS (fc06bc9) (GGML_CUDA_USE_GRAPHS=default)

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	61.110	67.03	93.308	10.97
4096	1024	4096	63.197	64.81	96.553	10.61
4096	1024	8192	65.332	62.69	101.798	10.06
4096	1024	12288	67.504	60.68	107.475	9.53
4096	1024	16384	69.694	58.77	113.288	9.04
4096	1024	20480	71.882	56.98	121.111	8.46
4096	1024	24576	74.105	55.27	128.320	7.98
4096	1024	28672	76.336	53.66	136.589	7.50

CUDA_GRAPHS (fc06bc9) (GGML_CUDA_USE_GRAPHS=off)

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	61.094	67.04	93.771	10.92
4096	1024	4096	63.175	64.84	96.601	10.60
4096	1024	8192	65.287	62.74	101.879	10.05
4096	1024	12288	67.474	60.71	107.304	9.54
4096	1024	16384	69.637	58.82	113.538	9.02
4096	1024	20480	71.847	57.01	121.361	8.44
4096	1024	24576	74.121	55.26	128.583	7.96
4096	1024	28672	76.310	53.68	135.303	7.57

HEAD (c519d41)

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	62.670	65.36	93.086	11.00
4096	1024	4096	64.768	63.24	96.333	10.63
4096	1024	8192	66.890	61.23	101.669	10.07
4096	1024	12288	69.054	59.32	107.363	9.54
4096	1024	16384	71.233	57.50	113.207	9.05
4096	1024	20480	73.414	55.79	121.171	8.45
4096	1024	24576	75.642	54.15	128.810	7.95
4096	1024	28672	77.859	52.61	136.124	7.52
4096	1024	32768	80.073	51.15	145.548	7.04

cmake -B build \
        -DCMAKE_BUILD_TYPE=Release \
        -DGGML_NATIVE=ON \
        -DGGML_CCACHE=OFF \
        -DGGML_CUDA=ON \
        -DBUILD_SHARED_LIBS=OFF \
        -DGGML_SCHED_MAX_COPIES=1 \
        -DGGML_VULKAN=OFF \
        -DGGML_RPC=OFF \
        -DGGML_BLAS=OFF \
        -DGGML_CUDA_F16=ON \
        -DGGML_CUDA_USE_GRAPHS=OFF \ 
        -DGGML_CUDA_IQK_FORCE_BF16=1

cmake --build build --config Release -j16

Name and Version

version: 3881 (c519d41)
built with cc (Debian 12.2.0-14+deb12u1) 12.2.0 for x86_64-linux-gnu

What operating system are you seeing the problem on?

Linux

Relevant log output

[ikawrakow]

I'm on vacation and will not be able to look into this for 2 weeks. But the CUDA graphs PR should not influence PP in any way, for MoE models it only enables graphs for TG, and it does not change how PP is done. If anything, the later PRs about PP optimisations for small batch sizes have a much higher chance of impacting Kimi PP performance.

[usrlocalben]

I was checking my work and noticed something that may narrow it down: Offload Policy.

The change observed in good/bad commit is the same as observed in modifying the offload policy.

modified policy:
-op 26,0,27,0,29,0 # slow pcie
XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op MULTI_ADD to OFF
XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op MUL_MAT to OFF
XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op OUT_PROD to OFF

Good = e082df4 (commit prior to PR #689)
Bad = fc06bc9 (PR #689)

Good Commit, Modified Offload Policy

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	44.273	92.52	93.525	10.95
4096	1024	4096	46.003	89.04	97.695	10.48

Good Commit, Default Offload Policy

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	61.211	66.92	93.331	10.97
4096	1024	4096	63.343	64.66	97.517	10.50

Bad Commit, Modified Offload Policy

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	61.076	67.06	93.309	10.97
4096	1024	4096	63.165	64.85	96.312	10.63

Bad Commit, Default Offload Policy

PP	TG	N_KV	T_PP s	S_PP t/s	T_TG s	S_TG t/s
4096	1024	0	61.103	67.03	93.172	10.99
4096	1024	4096	63.193	64.82	97.238	10.53

[Ph0rk0z]

-DGGML_CUDA_IQK_FORCE_BF16=1

This is theoretically a nono on turning.

[ikawrakow]

Oh, this is related to offloading or not offloading experts to the GPU. Looking at the output, the enum values for the operations have changed. What you want is to not offload MUL_MAT_ID and MOE_UP_GATE. Try -op 28,0,30,0

[sousekd]

Oh, this is related to offloading or not offloading experts to the GPU. Looking at the output, the enum values for the operations have changed. What you want is to not offload MUL_MAT_ID and MOE_UP_GATE. Try -op 28,0,30,0

Does this depend on the model? This is ubergarm/DeepSeek-R1-0528-IQ4_KS_R4 tested using EPYC 9355 + RTX 6000 on the current main branch (d323871) with:

./llama-sweep-bench \
    --model "$MODEL_PATH" \
    --no-mmap \
    -mla 3 -fa -fmoe \
    -amb 512 -b 8192 -ub 8192 \
    -ctk f16 \
    -c 262144 \
    -ngl 999 \
    -ot "blk\.[3-6]\.ffn_.*=CUDA0" \
    -ot exps=CPU \
    --parallel 2 \
    --threads 16 \
    --threads-batch 24 \
    --warmup-batch

-op 27,0,29,0

XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op MUL_MAT to OFF
XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op OUT_PROD to OFF

|    PP |     TG |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |
|-------|--------|--------|----------|----------|----------|----------|
|  8192 |   2048 |      0 |   14.798 |   553.57 |  119.408 |    17.15 |
|  8192 |   2048 |   8192 |   17.690 |   463.08 |  123.289 |    16.61 |

-op 28,0,30,0

XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op MUL_MAT_ID to OFF
XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op FUSED_UP_GATE to OFF

|    PP |     TG |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |
|-------|--------|--------|----------|----------|----------|----------|
|  8192 |   2048 |      0 |   33.092 |   247.55 |  121.040 |    16.92 |
|  8192 |   2048 |   8192 |   36.153 |   226.59 |  123.261 |    16.62 |

-op 28,0,31,0

XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op MUL_MAT_ID to OFF
XXXXXXXXXXXXXXXXXXXXX Setting offload policy for op MOE_FUSED_UP_GATE to OFF

|    PP |     TG |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |
|-------|--------|--------|----------|----------|----------|----------|
|  8192 |   2048 |      0 |   57.912 |   141.46 |  119.270 |    17.17 |
|  8192 |   2048 |   8192 |   60.734 |   134.88 |  122.775 |    16.68 |

without -op

|    PP |     TG |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |
|-------|--------|--------|----------|----------|----------|----------|
|  8192 |   2048 |      0 |   14.895 |   549.97 |  120.594 |    16.98 |
|  8192 |   2048 |   8192 |   17.838 |   459.24 |  124.075 |    16.51 |

Sorry I don't have Kimi K2 available at the moment.
And I did not manage to find MOE_UP_GATE :).

[ikawrakow]

And I did not manage to find MOE_UP_GATE :).

You did: -op 28,0,31,0. But you have a very fast GPU and PCIE, so not offloading is slower in your case.

[sousekd]

You did: -op 28,0,31,0. But you have a very fast GPU and PCIE, so not offloading is slower in your case.

Ah, okay. I know that -op 27,0,29,0 helped me on larger batch sizes in the past... on Windows.