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import os |
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import json |
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import gc |
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import torch |
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import pandas as pd |
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from datasets import load_dataset |
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from lmppl import EncoderDecoderLM, LM, OpenAI |
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", None) |
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prompt_dict = { |
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"friend/ally of": "entities that are friends or allies", |
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"competitor/rival of": "entities that are competitors or rivals", |
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"known for": "examples of what entities are known for", |
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"influenced by": "what has influenced different entities", |
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"similar to": "examples of entities that are similar" |
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} |
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data = load_dataset("cardiffnlp/relentless", split="test") |
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full_result = [] |
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for lm, ppl_class, batch, pretty_name in [ |
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("google/flan-t5-small", EncoderDecoderLM, 256, "Flan-T5\textsubscript{SMALL}"), |
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("google/flan-t5-base", EncoderDecoderLM, 128, "Flan-T5\textsubscript{BASE}"), |
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("google/flan-t5-large", EncoderDecoderLM, 32, "Flan-T5\textsubscript{LARGE}"), |
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("google/flan-t5-xl", EncoderDecoderLM, 1, "Flan-T5\textsubscript{XL}"), |
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("google/flan-t5-xxl", EncoderDecoderLM, 1, "Flan-T5\textsubscript{XXL}"), |
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("google/flan-ul2", EncoderDecoderLM, 1, "Flan-UL2"), |
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("t5-small", EncoderDecoderLM, 256, "T5\textsubscript{SMALL}"), |
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("t5-base", EncoderDecoderLM, 128, "T5\textsubscript{BASE}"), |
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("t5-large", EncoderDecoderLM, 32, "T5\textsubscript{LARGE}"), |
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("t5-3b", EncoderDecoderLM, 1, "T5\textsubscript{XL}"), |
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("t5-11b", EncoderDecoderLM, 1, "T5\textsubscript{XXL}"), |
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("facebook/opt-125m", LM, 256, "OPT\textsubscript{125M}"), |
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("facebook/opt-350m", LM, 128, "OPT\textsubscript{350M}"), |
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("facebook/opt-1.3b", LM, 1, "OPT\textsubscript{1.3B}"), |
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("facebook/opt-2.7b", LM, 1, "OPT\textsubscript{2.7B}"), |
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("facebook/opt-6.7b", LM, 1, "OPT\textsubscript{6.7B}"), |
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("facebook/opt-13b", LM, 1, "OPT\textsubscript{13B}"), |
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("facebook/opt-30b", LM, 1, "OPT\textsubscript{30B}"), |
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("facebook/opt-iml-1.3b", LM, 1, "OPT-IML\textsubscript{1.3B}"), |
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("facebook/opt-iml-30b", LM, 1, "OPT-IML\textsubscript{30B}"), |
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("facebook/opt-iml-max-1.3b", LM, 1, "OPT-IML\textsubscript{M-1.3B}"), |
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("facebook/opt-iml-max-30b", LM, 1, "OPT-IML\textsubscript{M-30B}"), |
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("davinci", OpenAI, None, "GPT-3\textsubscript{davinci}") |
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]: |
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os.makedirs(f"results/lm_qa/{os.path.basename(lm)}", exist_ok=True) |
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scorer = None |
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for d in data: |
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ppl_file = f"results/lm_qa/{os.path.basename(lm)}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl" |
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if not os.path.exists(ppl_file): |
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if scorer is None: |
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if ppl_class is OpenAI: |
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scorer = ppl_class(OPENAI_API_KEY, model=lm) |
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else: |
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scorer = ppl_class(lm, device_map='auto', low_cpu_mem_usage=True, offload_folder=f"./offload_folder/{os.path.basename(lm)}") |
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proto = ",".join([f'["{a}", "{b}"]' for a, b in d['prototypical_examples']]) |
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prefix = f"Answer the question by yes or no. We know that {proto} are examples of {prompt_dict[d['relation_type']]}." |
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if ppl_class is LM or ppl_class is OpenAI: |
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prompt_input = [f'{prefix} Are ["{x}", "{y}"] {prompt_dict[d["relation_type"]]} as well?\n yes' for x, y in d['pairs']] |
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ppl = scorer.get_perplexity(input_texts=prompt_input, batch=batch) |
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output = [{"perplexity": p, "input": i, "output": ""} for p, i in zip(ppl, prompt_input)] |
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elif ppl_class is EncoderDecoderLM: |
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prompt_input = [f'{prefix} Are ["{x}", "{y}"] {prompt_dict[d["relation_type"]]} as well?' for x, y in d['pairs']] |
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ppl = scorer.get_perplexity(input_texts=prompt_input, output_texts=["yes"] * len(prompt_input), batch=batch) |
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output = [{"perplexity": p, "input": o, "output": "yes"} for p, o in zip(ppl, prompt_input)] |
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else: |
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raise ValueError(f"Unknown class {ppl_class}") |
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with open(ppl_file, "w") as f: |
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f.write("\n".join([json.dumps(i) for i in output])) |
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with open(ppl_file) as f: |
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ppl = [json.loads(i)['perplexity'] for i in f.read().split("\n") if len(i) > 0] |
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true_rank = d['ranks'] |
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assert len(true_rank) == len(ppl), f"Mismatch in number of examples: {len(true_rank)} vs {len(ppl)}" |
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rank_map = {p: n for n, p in enumerate(sorted(ppl), 1)} |
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prediction = [rank_map[p] for p in ppl] |
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tmp = pd.DataFrame([true_rank, prediction], index=['true', 'pred']).T |
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cor = tmp.corr("spearman").values[0, 1] |
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full_result.append({"model": pretty_name, "relation_type": d['relation_type'], "correlation": cor}) |
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del scorer |
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gc.collect() |
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torch.cuda.empty_cache() |
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df = pd.DataFrame(full_result) |
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models = df['model'].unique() |
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df = df.pivot(columns="relation_type", index="model", values="correlation") |
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df = df.T[models].T |
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df['average'] = df.mean(1) |
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df.to_csv("results/lm_qa/lm.csv") |
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df = (100 * df).round() |
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print(df.to_markdown()) |
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print(df.to_latex(escape=False)) |
