91 lines
4.8 KiB
Markdown
91 lines
4.8 KiB
Markdown
# All in One Benchmark Test
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All in one benchmark test allows users to test all the benchmarks and record them in a result CSV. Users can provide models and related information in `config.yaml`.
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Before running, make sure to have [ipex-llm](../../../../../README.md) installed.
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> The prompts for benchmarking are from datasets [abisee/cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail), [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca), [THUDM/LongBench](https://huggingface.co/datasets/THUDM/LongBench), etc.
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## Dependencies
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```bash
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pip install omegaconf
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pip install pandas
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```
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Install gperftools to use libtcmalloc.so for MAX GPU to get better performance:
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```bash
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conda install -c conda-forge -y gperftools=2.10
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```
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## Config
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Config YAML file has following format
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```yaml
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repo_id:
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# - 'THUDM/chatglm2-6b'
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- 'meta-llama/Llama-2-7b-chat-hf'
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# - 'liuhaotian/llava-v1.5-7b' # requires a LLAVA_REPO_DIR env variables pointing to the llava dir; added only for gpu win related test_api now
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local_model_hub: 'path to your local model hub'
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warm_up: 1 # must set >=2 when run "pipeline_parallel_gpu" test_api
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num_trials: 3
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num_beams: 1 # default to greedy search
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low_bit: 'sym_int4' # default to use 'sym_int4' (i.e. symmetric int4)
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batch_size: 1 # default to 1
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in_out_pairs:
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- '32-32'
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- '1024-128'
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test_api:
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- "transformer_int4_fp16_gpu" # on Intel GPU, transformer-like API, (qtype=int4), (dtype=fp16)
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# - "transformer_int4_fp16_gpu_win" # on Intel GPU for Windows, transformer-like API, (qtype=int4), (dtype=fp16)
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# - "transformer_int4_gpu" # on Intel GPU, transformer-like API, (qtype=int4), (dtype=fp32)
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# - "transformer_int4_gpu_win" # on Intel GPU for Windows, transformer-like API, (qtype=int4), (dtype=fp32)
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# - "transformer_int4_loadlowbit_gpu_win" # on Intel GPU for Windows, transformer-like API, (qtype=int4), use load_low_bit API. Please make sure you have used the save.py to save the converted low bit model
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# - "transformer_int4_fp16_loadlowbit_gpu_win" # on Intel GPU for Windows, transformer-like API, (qtype=int4), (dtype=fp16), use load_low_bit API. Please make sure you have used the save.py to save the converted low bit model
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# - "bigdl_fp16_gpu" # on Intel GPU, use ipex-llm transformers API, (dtype=fp16), (qtype=fp16)
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# - "optimize_model_gpu" # on Intel GPU, can optimize any pytorch models include transformer model
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# - "deepspeed_optimize_model_gpu" # on Intel GPU, deepspeed autotp inference
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# - "pipeline_parallel_gpu" # on Intel GPU, pipeline parallel inference
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# - "speculative_gpu" # on Intel GPU, inference with self-speculative decoding
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# - "transformer_int4" # on Intel CPU, transformer-like API, (qtype=int4)
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# - "native_int4" # on Intel CPU
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# - "optimize_model" # on Intel CPU, can optimize any pytorch models include transformer model
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# - "pytorch_autocast_bf16" # on Intel CPU
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# - "transformer_autocast_bf16" # on Intel CPU
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# - "bigdl_ipex_bf16" # on Intel CPU, (qtype=bf16)
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# - "bigdl_ipex_int4" # on Intel CPU, (qtype=int4)
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# - "bigdl_ipex_int8" # on Intel CPU, (qtype=int8)
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# - "speculative_cpu" # on Intel CPU, inference with self-speculative decoding
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# - "deepspeed_transformer_int4_cpu" # on Intel CPU, deepspeed autotp inference
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# - "transformers_int4_npu_win" # on Intel NPU for Windows, transformer-like API, (qtype=int4)
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cpu_embedding: False # whether put embedding to CPU
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streaming: False # whether output in streaming way (only available now for gpu win related test_api)
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use_fp16_torch_dtype: True # whether use fp16 for non-linear layer (only available now for "pipeline_parallel_gpu" test_api)
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task: 'continuation' # task can be 'continuation', 'QA' and 'summarize'
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```
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## (Optional) Save model in low bit
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If you choose the `transformer_int4_loadlowbit_gpu_win` or `transformer_int4_fp16_loadlowbit_gpu_win` test API, you will need to save the model in low bit first.
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Running `python save.py` will save all models declared in `repo_id` list into low bit models under `local_model_hub` folder.
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## Run
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run `python run.py`, this will output results to `results.csv`.
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For SPR performance, run `bash run-spr.sh`.
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> **Note**
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>
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> The value of `OMP_NUM_THREADS` should be the same as the cpu cores specified by `numactl -C`.
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> **Note**
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>
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> Please install torch nightly version to avoid `Illegal instruction (core dumped)` issue, you can follow the following command to install: `pip install --pre --upgrade torch --index-url https://download.pytorch.org/whl/nightly/cpu`
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For ARC performance, run `bash run-arc.sh`.
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For MAX GPU performance, run `bash run-max-gpu.sh`.
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