Simple Accuracy Benchmark for Optimized LLMs

Was moved to the openvino.genai

Simple and quick accuracy test for compressed, quantized, pruned, distilled LLMs. It works with any model that suppors HuggingFace Transformers text generation API including:

• HuggingFace Transformers compressed models via Bitsandbytes
• GPTQ via HuggingFace API
• Llama.cpp via BigDL-LLM
• OpenVINO and NNCF via Optimum-Intel

The main idea is to compare similarity of text generation between baseline and optimized LLMs.

The API provides a way to access to investigate the worst generated text examples.

from transformers import AutoModelForCausalLM, AutoTokenizer
import whowhatbench

model_id = "facebook/opt-1.3b"
base_small = AutoModelForCausalLM.from_pretrained(model_id)
optimized_model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

evaluator = whowhatbench.Evaluator(base_model=base_small, tokenizer=tokenizer)
metrics_per_prompt, metrics = evaluator.score(optimized_model)

metric_of_interest = "similarity"
print(metric_of_interest, ": ", metrics["similarity"][0])

worst_examples = evaluator.worst_examples(top_k=5, metric=metric_of_interest)
print("Metric: ", metric_of_interest)
for e in worst_examples:
    print("\t=========================")
    print("\tPrompt: ", e["prompt"])
    print("\tBaseline Model:\n ", "\t" + e["source_model"])
    print("\tOptimized Model:\n ", "\t" + e["optimized_model"])

Use your own list of prompts to compare (e.g. from a dataset):

from datasets import load_dataset
val = load_dataset("lambada", split="validation[20:40]")
prompts = val["text"]
...
metrics_per_prompt, metrics = evaluator.score(optimized_model, test_data=prompts)

Installing

• git clone https://github.com/andreyanufr/who_what_benchmark.git
• python -m venv eval_env
• source eval_env/bin/activate
• pip install -r requirements.txt

CLI example

# run text generation for original model
python3 generate.py --modeltype causal --model meta-llama/Llama-2-7b-chat-hf --save_generations_path gold_llama-2-7b-chat-hf.csv --csv simple.csv --trust_remote_code

# convert and compress llama with optimum-intel and nncf and save it to some folder
...

#run text generation for compressed models
python3 generate.py --modeltype ov_causal --model /home/user/models/meta-llama/Llama-2-7b-chat-hf-int8 --save_generations_path predict_llama-2-7b-chat-hf_int8.csv --csv simple.csv --trust_remote_code

python3 generate.py --modeltype ov_causal --model /home/user/models/meta-llama/Llama-2-7b-chat-hf-int4_sym --save_generations_path predict_llama-2-7b-chat-hf_int4_sym.csv --csv simple.csv --trust_remote_code

python3 generate.py --modeltype ov_causal --model /home/user/models/meta-llama/Llama-2-7b-chat-hf-int4_asym --save_generations_path predict_llama-2-7b-chat-hf_int4_asym.csv --csv simple.csv --trust_remote_code


for file in predict_llama-2-7b*; do
python3 evaluate.py --gold gold_llama-2-7b-chat-hf.csv --prediction $file --save_evaluation_path eval_$file 2>&1 | tee -a eval.log
done

Supported metrics

• similarity - averaged similarity measured by neural network trained for sentence embeddings. The best is 1.0, the minimum is 0.0, higher-better.
• FDT - Average position of the first divergent token betwen sentences generated by differnrt LLMs. The worst is 0, higher-better. Paper.
• FDT norm - Average share of matched tokens until first divergent one betwen sentences generated by differnrt LLMs. The best is 1, higher-better.Paper.
• SDT - Average number of divergent tokens in the evaluated outputs betwen sentences generated by differnrt LLMs. The best is 0, lower-better. Paper.
• SDT norm - Average share of divergent tokens in the evaluated outputs betwen sentences generated by differnrt LLMs. The best is 0, the maximum is 1, lower-better. Paper.

Notes

• In the file save_evaluation_path you can see per sample similarity metrics.
• Input CSV file for generation must contain column with name questions. For example see simple.csv
• You can see example of generation in file generations.csv
• evaluate.py uses for similarity measurement sentence-transformers/all-mpnet-base-v2 but you can use other similar network.