import os import json import gc import torch import pandas as pd from datasets import load_dataset from lmppl import EncoderDecoderLM, LM, OpenAI OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", None) prompt_dict = { "friend/ally of": "Complete the following list with examples of entities that are friends or allies", "competitor/rival of": "Complete the following list with examples of entities that are competitors or rivals", "known for": "Complete the following list with examples of what entities are known for", "influenced by": "Complete the following list with examples of what has influenced different entities", "similar to": "Complete the following list with examples of entities that are similar" } data = load_dataset("cardiffnlp/relentless", split="test") full_result = [] for lm, ppl_class, batch, pretty_name in [ ("google/flan-t5-small", EncoderDecoderLM, 256, "Flan-T5\textsubscript{SMALL}"), ("google/flan-t5-base", EncoderDecoderLM, 128, "Flan-T5\textsubscript{BASE}"), ("google/flan-t5-large", EncoderDecoderLM, 32, "Flan-T5\textsubscript{LARGE}"), ("google/flan-t5-xl", EncoderDecoderLM, 1, "Flan-T5\textsubscript{XL}"), ("google/flan-t5-xxl", EncoderDecoderLM, 1, "Flan-T5\textsubscript{XXL}"), ("google/flan-ul2", EncoderDecoderLM, 1, "Flan-UL2"), ("t5-small", EncoderDecoderLM, 256, "T5\textsubscript{SMALL}"), ("t5-base", EncoderDecoderLM, 128, "T5\textsubscript{BASE}"), ("t5-large", EncoderDecoderLM, 32, "T5\textsubscript{LARGE}"), ("t5-3b", EncoderDecoderLM, 1, "T5\textsubscript{XL}"), ("t5-11b", EncoderDecoderLM, 1, "T5\textsubscript{XXL}"), ("facebook/opt-125m", LM, 256, "OPT\textsubscript{125M}"), ("facebook/opt-350m", LM, 128, "OPT\textsubscript{350M}"), ("facebook/opt-1.3b", LM, 1, "OPT\textsubscript{1.3B}"), ("facebook/opt-2.7b", LM, 1, "OPT\textsubscript{2.7B}"), ("facebook/opt-6.7b", LM, 1, "OPT\textsubscript{6.7B}"), ("facebook/opt-13b", LM, 1, "OPT\textsubscript{13B}"), ("facebook/opt-30b", LM, 1, "OPT\textsubscript{30B}"), # ("facebook/opt-66b", LM, 1, "OPT\textsubscript{66B}"), ("facebook/opt-iml-1.3b", LM, 1, "OPT-IML\textsubscript{1.3B}"), ("facebook/opt-iml-30b", LM, 1, "OPT-IML\textsubscript{30B}"), ("facebook/opt-iml-max-1.3b", LM, 1, "OPT-IML\textsubscript{M-1.3B}"), ("facebook/opt-iml-max-30b", LM, 1, "OPT-IML\textsubscript{M-30B}"), ("davinci", OpenAI, None, "GPT-3\textsubscript{davinci}") ]: os.makedirs(f"results/lm_lc/{os.path.basename(lm)}", exist_ok=True) scorer = None for d in data: ppl_file = f"results/lm_lc/{os.path.basename(lm)}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl" if not os.path.exists(ppl_file): if scorer is None: if ppl_class is OpenAI: scorer = ppl_class(OPENAI_API_KEY, model=lm) else: scorer = ppl_class(lm, device_map='auto', low_cpu_mem_usage=True, offload_folder=f"./offload_folder/{os.path.basename(lm)}") content = "\n".join([f'* ["{a}", "{b}"]' for a, b in d['prototypical_examples']]) prompt_input = f"{prompt_dict[d['relation_type']]}:\n{content}" if ppl_class is LM: prompt_input = [f'{prompt_input}\n* ["{x}", "{y}"]' for x, y in d['pairs']] ppl = scorer.get_perplexity(input_texts=prompt_input, batch=batch) output = [{"perplexity": p, "input": i, "output": ""} for p, i in zip(ppl, prompt_input)] elif ppl_class is EncoderDecoderLM: prompt_output = [f'* ["{x}", "{y}"]' for x, y in d['pairs']] ppl = scorer.get_perplexity(input_texts=[prompt_input] * len(prompt_output), output_texts=prompt_output, batch=batch) output = [{"perplexity": p, "input": prompt_input, "output": o} for p, o in zip(ppl, prompt_output)] else: prompt_input = [f'{prompt_input}\n* ["{x}", "{y}"]' for x, y in d['pairs']] ppl = scorer.get_perplexity(input_texts=prompt_input) output = [{"perplexity": p, "input": i, "output": ""} for p, i in zip(ppl, prompt_input)] with open(ppl_file, "w") as f: f.write("\n".join([json.dumps(i) for i in output])) with open(ppl_file) as f: ppl = [json.loads(i)['perplexity'] for i in f.read().split("\n") if len(i) > 0] true_rank = d['ranks'] assert len(true_rank) == len(ppl), f"Mismatch in number of examples: {len(true_rank)} vs {len(ppl)}" rank_map = {p: n for n, p in enumerate(sorted(ppl), 1)} prediction = [rank_map[p] for p in ppl] tmp = pd.DataFrame([true_rank, prediction], index=['true', 'pred']).T cor = tmp.corr("spearman").values[0, 1] full_result.append({"model": pretty_name, "relation_type": d['relation_type'], "correlation": cor}) del scorer gc.collect() torch.cuda.empty_cache() df = pd.DataFrame(full_result) models = df['model'].unique() print(df) df = df.pivot(columns="relation_type", index="model", values="correlation") df = df.T[models].T df['average'] = df.mean(1) df.to_csv("results/lm_lc/lm.csv") df = (100 * df).round() print(df.to_markdown()) print(df.to_latex(escape=False))