mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2025-09-05 20:19:51 +00:00
64 lines
3.5 KiB
Markdown
64 lines
3.5 KiB
Markdown
## prerequisites
|
||
We run our best performance tests on <br>
|
||
cpu: Intel(R) Xeon(R) Gold 6454S 1T DRAM(2 NUMA nodes)<br>
|
||
gpu: 4090D 24G VRAM <br>
|
||
## bench result
|
||
### V0.2
|
||
#### settings
|
||
- model: DeepseekV3-q4km（int4）<br>
|
||
- CPU: cpu_model_name：Intel(R) Xeon(R) Gold 6454S, 32 cores per socket, 2 socket, 2numa nodes
|
||
- GPU: 4090D 24GVRAM
|
||
- we test after enough warm up!
|
||
#### memory consumption:
|
||
- single socket: 382G DRAM, 12G VRAM
|
||
- dual socket: 1T DRAM, 12G VRAM
|
||
|
||
#### Benchmark Results
|
||
|
||
"6 experts" case is part of v0.3's preview
|
||
|
||
| Prompt<br>(500 tokens) | Dual socket Ktrans (6 experts) | Dual socket Ktrans (8 experts) | Single socket Ktrans (6 experts) | Single socket Ktrans (8 experts)| Llama (8 experts) |
|
||
| --- | --- | --- | --- | --- | --- |
|
||
| Prefill token/s | 97.32 | 82.94 | 65.14 | 54.21 | 10.31 |
|
||
| Decode token/s | 13.69 | 12.208 | 10.303 | 8.73 |4.51 |
|
||
|
||
**The highest speedup reaches up to <u>x3.03</u> in decoding and <u>x9.44</u> in prefill.**
|
||
|
||
## how to run
|
||
### v0.2 showcase
|
||
#### single socket version(32 cores)
|
||
our local_chat test command is:
|
||
``` shell
|
||
git clone https://github.com/kvcache-ai/ktransformers.git
|
||
cd ktransformers
|
||
numactl -N 1 -m 1 python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your promt txt file> --cpu_infer 33 --cache_lens 1536
|
||
<when you see chat, then press enter to load the text prompt_file>
|
||
```
|
||
\<your model path\> can be local or set from onlie hugging face like deepseek-ai/DeepSeek-V3. If onlie encounters connection problem, try use mirror(hf-mirror.com) <br>
|
||
\<your gguf path\> can also be onlie, but as its large we recommend you download it and quantize the model to what you want.<br>
|
||
the command numactl -N 1 -m 1 aims to adoid data transfer between numa nodes.
|
||
### dual socket version(64 cores)
|
||
make suer before you install(use install.sh or `make dev_install`), setting the env var `USE_NUMA=1` by `export USE_NUMA=1`(if already installed, reinstall it with this env var set) <br>
|
||
our local_chat test command is:
|
||
``` shell
|
||
git clone https://github.com/kvcache-ai/ktransformers.git
|
||
cd ktransformers
|
||
export USE_NUMA=1
|
||
make dev_install # or sh ./install.sh
|
||
python ./ktransformers/local_chat.py --model_path <your model path> --gguf_path <your gguf path> --prompt_file <your promt txt file> --cpu_infer 65 --cache_lens 1536
|
||
<when you see chat, then press enter to load the text prompt_file>
|
||
```
|
||
The parameters meaning is the same. But As we use dual socket, so we set cpu_infer to 65.
|
||
## some explanations
|
||
1. From our perspective on DeepSeekV2, DeepSeekV3 and DeepSeekR1,
|
||
when we slightly decrease the activation experts num in inference,
|
||
the output quality doesn't change(within 1% accuracy drop),But the speed of decoding and prefill
|
||
is speed up about 30% which is inspiring. So our showcase makes use of this finding,
|
||
changing the activation experts of DeepSeekV3/R1 from 8 to 6. <br>
|
||
2. Also we want to make further use of our two NUMA nodes on Xeon Gold cpu.
|
||
To avoid the cost of data transfer between nodes, we "copy" the critical matrix on
|
||
both nodes which takes more memory consumption but accelerates the prefill and decoding process.
|
||
But this method takes huge memory and slow when loading weights, So be patient when loading
|
||
and monitor the memory usage.(we are considering to make this method as an option)<br>
|
||
3. the command args `--cpu_infer 65` specifies how many cores to use(it's ok that it exceeds the physical number,
|
||
but it's not the more the better. Adjust it slight lower to your actual number of cores)<br>
|