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import argparse
import json
import os
import time
import numpy as np
import pandas as pd
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from categories import categories, subcategories
choices = ["A", "B", "C", "D"]
def format_subject(subject):
l = subject.split("_")
s = ""
for entry in l:
s += " " + entry
return s
def format_example(df, idx, include_answer=True):
prompt = df.iloc[idx, 0]
k = df.shape[1] - 2
for j in range(k):
prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j + 1])
prompt += "\nAnswer:"
if include_answer:
prompt += " {}\n\n".format(df.iloc[idx, k + 1])
return prompt
def gen_prompt(train_df, subject, k=-1):
prompt = "The following are multiple choice questions (with answers) about {}.\n\n".format(
format_subject(subject)
)
if k == -1:
k = train_df.shape[0]
for i in range(k):
prompt += format_example(train_df, i)
return prompt
@torch.no_grad()
def eval(args, subject, model, tokenizer, dev_df, test_df):
cors = []
all_probs = []
answers = choices[: test_df.shape[1] - 2]
for i in range(test_df.shape[0]):
k = args.ntrain
prompt_end = format_example(test_df, i, include_answer=False)
train_prompt = gen_prompt(dev_df, subject, k)
prompt = train_prompt + prompt_end
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
while input_ids.shape[-1] > 2048:
k -= 1
train_prompt = gen_prompt(dev_df, subject, k)
prompt = train_prompt + prompt_end
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(
model.device
)
label = test_df.iloc[i, test_df.shape[1] - 1]
logits = model(input_ids=input_ids).logits[0, -1]
probs = (
torch.nn.functional.softmax(
torch.tensor(
[
logits[tokenizer("A").input_ids[-1]],
logits[tokenizer("B").input_ids[-1]],
logits[tokenizer("C").input_ids[-1]],
logits[tokenizer("D").input_ids[-1]],
]
).float(),
dim=0,
)
.detach()
.cpu()
.numpy()
)
pred = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(probs)]
cor = pred == label
cors.append(cor)
all_probs.append(probs)
acc = np.mean(cors)
cors = np.array(cors)
all_probs = np.array(all_probs)
print("Average accuracy {:.3f} - {}".format(acc, subject))
return cors, acc, all_probs
def main(args):
model = AutoModelForCausalLM.from_pretrained(
args.model,
torch_dtype=torch.float16,
load_in_8bit=False,
low_cpu_mem_usage=True,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
model.eval()
subjects = sorted(
[
f.split("_test.csv")[0]
for f in os.listdir(os.path.join(args.data_dir, "test"))
if "_test.csv" in f
]
)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
if not os.path.exists(os.path.join(args.save_dir, "results_{}".format(args.model.split("/")[-1]))):
os.makedirs(os.path.join(args.save_dir, "results_{}".format(args.model.split("/")[-1])))
all_cors = []
subcat_cors = {
subcat: [] for subcat_lists in subcategories.values() for subcat in subcat_lists
}
cat_cors = {cat: [] for cat in categories}
start_time = time.time()
for subject in subjects:
dev_df = pd.read_csv(
os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None
)[: args.ntrain]
test_df = pd.read_csv(
os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None
)
cors, acc, probs = eval(args, subject, model, tokenizer, dev_df, test_df)
subcats = subcategories[subject]
for subcat in subcats:
subcat_cors[subcat].append(cors)
for key in categories.keys():
if subcat in categories[key]:
cat_cors[key].append(cors)
all_cors.append(cors)
test_df["{}_correct".format(args.model)] = cors
for j in range(probs.shape[1]):
choice = choices[j]
test_df["{}_choice{}_probs".format(args.model, choice)] = probs[:, j]
test_df.to_csv(
os.path.join(
args.save_dir, "results_{}".format(args.model.split("/")[-1]), "{}.csv".format(subject)
),
index=None,
)
results = {"subcategories": {}, "categories": {}}
for subcat in subcat_cors:
subcat_acc = np.mean(np.concatenate(subcat_cors[subcat]))
results["subcategories"][subcat] = subcat_acc
print("Average accuracy {:.3f} - {}".format(subcat_acc, subcat))
for cat in cat_cors:
cat_acc = np.mean(np.concatenate(cat_cors[cat]))
results["categories"][cat] = cat_acc
print("Average accuracy {:.3f} - {}".format(cat_acc, cat))
weighted_acc = np.mean(np.concatenate(all_cors))
results["weighted_accuracy"] = weighted_acc
print("Average accuracy: {:.3f}".format(weighted_acc))
results_file = os.path.join(
args.save_dir, "accuracies_{}.json".format(args.model.replace("/", "_"))
)
end_time = time.time()
results["cost_time"] = end_time - start_time
with open(results_file, "w") as f:
f.write(json.dumps(results, indent=4))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--data_dir", "-d", type=str, default="data")
parser.add_argument("--save_dir", "-s", type=str, default="results")
parser.add_argument("--model", "-m", type=str)
args = parser.parse_args()
main(args)
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