| import os |
| import json |
| from random import shuffle, seed |
| from itertools import permutations |
| import pandas as pd |
| from datasets import load_dataset |
| from lmppl import EncoderDecoderLM, LM, OpenAI |
|
|
| OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", None) |
| runs = 3 |
| shots_num = [1, 3] |
| prompt_dict = { |
| "friend/ally of": "entities that are friends or allies", |
| "competitor/rival of": "entities that are competitors or rivals", |
| "known for": "examples of what entities are known for", |
| "influenced by": "what has influenced different entities", |
| "similar to": "examples of entities that are similar" |
| } |
| data = load_dataset("cardiffnlp/relentless", split="test") |
| shots_ref = {} |
| for shots in shots_num: |
| all_perms = list(permutations(range(5), shots)) |
| seed(42) |
| shuffle(all_perms) |
| shots_ref[shots] = all_perms |
|
|
| full_result = [] |
| for lm, ppl_class, batch, pretty_name in [ |
| ("google/flan-ul2", EncoderDecoderLM, 1, "Flan-UL2"), |
| ("google/flan-t5-xxl", EncoderDecoderLM, 1, "Flan-T5\textsubscript{XXL}"), |
| ("facebook/opt-13b", LM, 1, "OPT\textsubscript{13B}"), |
| ("davinci", OpenAI, None, "GPT-3\textsubscript{davinci}") |
| ]: |
| scorer = None |
| for shots in shots_num: |
| for s in range(runs): |
| os.makedirs(f"results/lm_qa_{shots}shots_{s}seed/{os.path.basename(lm)}", exist_ok=True) |
| for d in data: |
| ppl_file = f"results/lm_qa_{shots}shots_{s}seed/{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)}") |
| demo = [d['prototypical_examples'][h] for h in list(shots_ref[shots][s])] |
| proto = ",".join([f'["{a}", "{b}"]' for a, b in demo]) |
| prefix = f"Answer the question by yes or no. We know that {proto} are examples of {prompt_dict[d['relation_type']]}." |
| if ppl_class is LM or ppl_class is OpenAI: |
| prompt_input = [f'{prefix} Are ["{x}", "{y}"] {prompt_dict[d["relation_type"]]} as well?\n yes' 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_input = [f'{prefix} Are ["{x}", "{y}"] {prompt_dict[d["relation_type"]]} as well?' for x, y in d['pairs']] |
| ppl = scorer.get_perplexity(input_texts=prompt_input, output_texts=["yes"] * len(prompt_input), batch=batch) |
| output = [{"perplexity": p, "input": o, "output": "yes"} for p, o in zip(ppl, prompt_input)] |
| else: |
| raise ValueError(f"Unknown class {ppl_class}") |
|
|
| 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, "shot": shots, "seed": s, "relation_type": d['relation_type'], "correlation": cor}) |
|
|
| df = pd.DataFrame(full_result) |
| models = df['model'].unique() |
| df = df.pivot(columns="relation_type", index=["model", "shot", "seed"], values="correlation") |
| df = df.T[models].T |
| df['average'] = df.mean(1) |
| df.to_csv(f"results/lm_qa_fewshots.csv") |
| df = (100 * df).round() |
| print(df) |
| print(df.to_markdown()) |
| print(df.to_latex(escape=False)) |
|
|
