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# !pip install -q transformers datasets sentencepiece
import argparse
import gc
import json
import os
import datasets
import pandas as pd
import torch
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer
TOTAL_NUM_FILES_C4_TRAIN = 1024
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--start",
type=int,
required=True,
help="Starting file number to download. Valid values: 0 - 1023",
)
parser.add_argument(
"--end",
type=int,
required=True,
help="Ending file number to download. Valid values: 0 - 1023",
)
parser.add_argument("--batch_size", type=int, default=8, help="Batch size")
parser.add_argument(
"--model_name",
type=str,
default="taskydata/deberta-v3-base_10xp3nirstbbflanseuni_10xc4",
help="Model name",
)
parser.add_argument(
"--local_cache_location",
type=str,
default="c4_download",
help="local cache location from where the dataset will be loaded",
)
parser.add_argument(
"--use_local_cache_location",
type=bool,
default=True,
help="Set True if you want to load the dataset from local cache.",
)
parser.add_argument(
"--clear_dataset_cache",
type=bool,
default=False,
help="Set True if you want to delete the dataset files from the cache after inference.",
)
parser.add_argument(
"--release_memory",
type=bool,
default=True,
help="Set True if you want to release the memory of used variables.",
)
args = parser.parse_args()
return args
def chunks(l, n):
for i in range(0, len(l), n):
yield l[i : i + n]
def batch_tokenize(data, batch_size):
batches = list(chunks(data, batch_size))
tokenized_batches = []
for batch in batches:
# max_length will automatically be set to the max length of the model (512 for deberta)
tensor = tokenizer(
batch,
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=512,
)
tokenized_batches.append(tensor)
return tokenized_batches, batches
def batch_inference(data, batch_size=32):
preds = []
tokenized_batches, batches = batch_tokenize(data, batch_size)
for i in tqdm(range(len(batches))):
with torch.no_grad():
logits = model(**tokenized_batches[i].to(device)).logits.cpu()
preds.extend(logits)
return preds
if __name__ == "__main__":
args = parse_args()
tasky_commits_path = f"tasky_commits_python_{args.start}_{args.end}.jsonl"
if os.path.exists(f"python_add/{tasky_commits_path}"):
print("Exists:", tasky_commits_path)
exit()
path = "python_add_messages.jsonl"
ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
if args.start > len(ds): exit()
ds = ds[range(args.start, min(args.end, len(ds)))]
df = pd.DataFrame(ds, index=None)
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
model.eval()
#path = "python_add_messages.jsonl"
#ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
#ds = ds[range(args.start, min(args.end, len(ds)))]
#df = pd.DataFrame(ds, index=None)
texts = df["message"].to_list()
commits = df["commit"].to_list()
preds = batch_inference(texts, batch_size=args.batch_size)
assert len(preds) == len(texts)
# Write two jsonl files:
# 1) Probas for all of C4
# 2) Probas + texts for samples predicted as tasky
tasky_commits_path = f"python_add/tasky_commits_python_{args.start}_{args.end}.jsonl"
with open(tasky_commits_path, "w") as f:
for i in range(len(preds)):
predicted_class_id = preds[i].argmax().item()
pred = model.config.id2label[predicted_class_id]
tasky_proba = torch.softmax(preds[i], dim=-1)[-1].item()
f.write(
json.dumps(
{
"commit": commits[i],
"message": texts[i],
"proba": tasky_proba,
}
)
+ "\n"
)
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