68 lines
		
	
	
	
		
			2 KiB
		
	
	
	
		
			Markdown
		
	
	
	
	
	
			
		
		
	
	
			68 lines
		
	
	
	
		
			2 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 [bigdl-llm](../../../README.md).
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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/chatglm-6b'
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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
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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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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"
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  - "native_int4"
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  - "optimize_model"
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  - "pytorch_autocast_bf16"
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  # - "transformer_autocast_bf16"
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  # - "ipex_fp16_gpu" # on Intel GPU
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  # - "transformer_int4_gpu"  # on Intel GPU
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  # - "optimize_model_gpu"  # on Intel GPU
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  # - "deepspeed_transformer_int4_cpu" # on Intel SPR Server
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  # - "transformer_int4_gpu_win" # on Intel GPU for Windows (catch GPU peak memory)
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cpu_embedding: False # whether put embedding to CPU (only avaiable now for gpu win related test_api)
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```
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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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