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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-ul2", EncoderDecoderLM, 1, "Flan-UL2"),
("google/flan-t5-xxl", EncoderDecoderLM, 1, "Flan-T5\textsubscript{XXL}"),
("google/flan-t5-xl", EncoderDecoderLM, 1, "Flan-T5\textsubscript{XL}"),
("google/flan-t5-large", EncoderDecoderLM, 32, "Flan-T5\textsubscript{LARGE}"),
("google/flan-t5-base", EncoderDecoderLM, 128, "Flan-T5\textsubscript{BASE}"),
("google/flan-t5-small", EncoderDecoderLM, 256, "Flan-T5\textsubscript{SMALL}"),
("t5-11b", EncoderDecoderLM, 1, "T5\textsubscript{XXL}"),
("t5-3b", EncoderDecoderLM, 1, "T5\textsubscript{XL}"),
("t5-large", EncoderDecoderLM, 32, "T5\textsubscript{LARGE}"),
("t5-base", EncoderDecoderLM, 128, "T5\textsubscript{BASE}"),
("t5-small", EncoderDecoderLM, 256, "T5\textsubscript{SMALL}"),
# ("facebook/opt-66b", LM, 1, "OPT\textsubscript{66B}"),
("facebook/opt-30b", LM, 1, "OPT\textsubscript{30B}"),
("facebook/opt-13b", LM, 1, "OPT\textsubscript{13B}"),
("facebook/opt-6.7b", LM, 1, "OPT\textsubscript{6.7B}"),
("facebook/opt-2.7b", LM, 1, "OPT\textsubscript{2.7B}"),
("facebook/opt-1.3b", LM, 1, "OPT\textsubscript{1.3B}"),
("facebook/opt-350m", LM, 128, "OPT\textsubscript{350M}"),
("facebook/opt-125m", LM, 256, "OPT\textsubscript{125M}"),
("facebook/opt-iml-30b", LM, 1, "OPT-IML\textsubscript{30B}"),
("facebook/opt-iml-1.3b", LM, 1, "OPT-IML\textsubscript{1.3B}"),
("facebook/opt-iml-max-30b", LM, 1, "OPT-IML\textsubscript{MAX-30B}"),
("facebook/opt-iml-max-1.3b", LM, 1, "OPT-IML\textsubscript{MAX-1.3B}"),
# ("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))
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