|
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": "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") |
|
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-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}"), |
|
|
|
]: |
|
os.makedirs(f"results/lm_qa/{os.path.basename(lm)}", exist_ok=True) |
|
scorer = None |
|
for d in data: |
|
ppl_file = f"results/lm_qa/{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)}") |
|
|
|
proto = ",".join([f'["{a}", "{b}"]' for a, b in d['prototypical_examples']]) |
|
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, "relation_type": d['relation_type'], "correlation": cor}) |
|
del scorer |
|
gc.collect() |
|
torch.cuda.empty_cache() |
|
|
|
df = pd.DataFrame(full_result) |
|
models = df['model'].unique() |
|
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_qa/lm.csv") |
|
df = (100 * df).round() |
|
print(df.to_markdown()) |
|
print(df.to_latex(escape=False)) |
