File size: 4,795 Bytes
6fbb8ba 0f6d058 6fbb8ba 5c627fa 511c4b4 5c627fa 0f6d058 6fbb8ba 0f6d058 5c627fa 6fbb8ba 0f6d058 6fbb8ba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
# !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_javascript_{args.start}_{args.end}.jsonl"
if os.path.exists(f"javascript_add/{tasky_commits_path}"):
print("Exists:", tasky_commits_path)
exit()
path = "javascript_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 = "javascript_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)
#tasky_commits_path = f"tasky_commits_javascript_{args.start}_{args.end}.jsonl"
#if os.path.exists(f"javascript/{tasky_commits_path}"):
# print("Exists:", tasky_commits_path)
# exit()
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"javascript_add/tasky_commits_javascript_{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"
)
|