We take the [Chinese-LLaMa-Alpaca 7B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) instruction model as the benchmark target. The benchmark process is as follows:
1. Use the `deploy.py` to convert the model, modify the maximum concurrent amount in `workspace`, and adjust the request amount in `llama_config.ini`
2. Compile the `bin/llama_triton_example` and get the graphics usage status of the fp16 version model under different batch_size settings
3. Execute the quantization script and get the quantization parameters. Then modify the config file to make [kCacheKVInt8](../../src/turbomind/models/llama/llama_utils.h) be effective
4. Re-execute the `bin/llama_triton_example` and get the graphics usage status of the int8 version model under different batch_size settings
Here is the benchmark result between the two versions of the model:
To compare with the weight quantization method such as [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa/) , we benchmarked the memory usages of the 7B model between the two solutions, with part of the data from [llama.cpp](https://github.com/ggerganov/llama.cpp) . Here is the result:
Since each concurrency requires 1030MB of the graphics memory to save the kv_cache for 2048 tokens, and the server side needs to consider the cost of high concurrency scenarios, it is more appropriate to run kv_cache quantization rather than directly quantize the weights.
Note that `kCacheKVInt8` and `WeightInt4` can be used simultaneously, and we will provide relevant implementations later.
## Benchmark the Accuracy
Here we take the [Chinese-LLaMa-Alpaca 7B](https://github.com/ymcui/Chinese-LLaMA-Alpaca) instruction model as the benchmark target again. The benchmark process is as follows:
1. Convert the model with `deploy.py` and run the docker service
2. Test the fp16 version accuracy with `client.py` using the dataset
3. Execute the quantization script to get the quantization parameters, and put them into the weights directory. Then modify the configuration file to make [kCacheKVInt8](../../src/turbomind/models/llama/llama_utils.h) option to be effective
4. Execute the `client.py` again to get the int8 version precision
The following table is the precision result obtained by the `kCacheKVInt8` method after quantizing 128 randomly selected data from the c4 dataset and testing it on the mmlu-social-science dataset, which has a total of 3065 multiple-choice questions:
