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  1. .gitattributes +188 -0
  2. inference.py +138 -0
  3. inference.sh +25 -0
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.gitattributes CHANGED
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+ java/tasky_commits_1711268_1744177.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2599811_2632720.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2632720_2665629.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_2237812_2270721.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_1151815_1184724.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2402357_2435266.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_460726_493635.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_691089_723998.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_1908722_1941631.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_460726_493635.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_1184724_1217633.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_987270_1020179.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_1645450_1678359.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2171994_2204903.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_296181_329090.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_3257991_3290900.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_822725_855634.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_921452_954361.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_987270_1020179.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_2336539_2369448.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_2566902_2599811.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_526544_559453.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_164545_197454.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2369448_2402357.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_329090_361999.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_427817_460726.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_526544_559453.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_2139085_2171994.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2139085_2171994.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2863083_2895992.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_658180_691089.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_493635_526544.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_1908722_1941631.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2040358_2073267.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_2303630_2336539.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_3126355_3159264.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_3159264_3192173.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_361999_394908.jsonl filter=lfs diff=lfs merge=lfs -text
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+ java/tasky_commits_493635_526544.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_1415087_1447996.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_2204903_2237812.jsonl filter=lfs diff=lfs merge=lfs -text
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+ python/tasky_commits_python_32909_65818.jsonl filter=lfs diff=lfs merge=lfs -text
inference.py ADDED
@@ -0,0 +1,138 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # !pip install -q transformers datasets sentencepiece
2
+ import argparse
3
+ import gc
4
+ import json
5
+ import os
6
+
7
+ import datasets
8
+ import pandas as pd
9
+ import torch
10
+ from tqdm import tqdm
11
+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
12
+
13
+ TOTAL_NUM_FILES_C4_TRAIN = 1024
14
+
15
+
16
+ def parse_args():
17
+ parser = argparse.ArgumentParser()
18
+
19
+ parser.add_argument(
20
+ "--start",
21
+ type=int,
22
+ required=True,
23
+ help="Starting file number to download. Valid values: 0 - 1023",
24
+ )
25
+ parser.add_argument(
26
+ "--end",
27
+ type=int,
28
+ required=True,
29
+ help="Ending file number to download. Valid values: 0 - 1023",
30
+ )
31
+ parser.add_argument("--batch_size", type=int, default=64, help="Batch size")
32
+ parser.add_argument(
33
+ "--model_name",
34
+ type=str,
35
+ default="taskydata/deberta-v3-base_10xp3nirstbbflanseuni_10xc4",
36
+ help="Model name",
37
+ )
38
+ parser.add_argument(
39
+ "--local_cache_location",
40
+ type=str,
41
+ default="c4_download",
42
+ help="local cache location from where the dataset will be loaded",
43
+ )
44
+ parser.add_argument(
45
+ "--use_local_cache_location",
46
+ type=bool,
47
+ default=True,
48
+ help="Set True if you want to load the dataset from local cache.",
49
+ )
50
+ parser.add_argument(
51
+ "--clear_dataset_cache",
52
+ type=bool,
53
+ default=False,
54
+ help="Set True if you want to delete the dataset files from the cache after inference.",
55
+ )
56
+ parser.add_argument(
57
+ "--release_memory",
58
+ type=bool,
59
+ default=True,
60
+ help="Set True if you want to release the memory of used variables.",
61
+ )
62
+
63
+ args = parser.parse_args()
64
+ return args
65
+
66
+
67
+ def chunks(l, n):
68
+ for i in range(0, len(l), n):
69
+ yield l[i : i + n]
70
+
71
+
72
+ def batch_tokenize(data, batch_size):
73
+ batches = list(chunks(data, batch_size))
74
+ tokenized_batches = []
75
+ for batch in batches:
76
+ # max_length will automatically be set to the max length of the model (512 for deberta)
77
+ tensor = tokenizer(
78
+ batch,
79
+ return_tensors="pt",
80
+ padding="max_length",
81
+ truncation=True,
82
+ max_length=512,
83
+ )
84
+ tokenized_batches.append(tensor)
85
+ return tokenized_batches, batches
86
+
87
+
88
+ def batch_inference(data, batch_size=32):
89
+ preds = []
90
+ tokenized_batches, batches = batch_tokenize(data, batch_size)
91
+ for i in tqdm(range(len(batches))):
92
+ with torch.no_grad():
93
+ logits = model(**tokenized_batches[i].to(device)).logits.cpu()
94
+ preds.extend(logits)
95
+ return preds
96
+
97
+
98
+ if __name__ == "__main__":
99
+ args = parse_args()
100
+
101
+ tokenizer = AutoTokenizer.from_pretrained(args.model_name)
102
+ model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
103
+ device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
104
+ model.to(device)
105
+ model.eval()
106
+
107
+ path = "python_messages.jsonl"
108
+ ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
109
+ ds = ds[range(args.start, args.end)]
110
+ df = pd.DataFrame(ds, index=None)
111
+ texts = df["message"].to_list()
112
+ commits = df["commit"].to_list()
113
+ preds = batch_inference(texts, batch_size=args.batch_size)
114
+
115
+ assert len(preds) == len(texts)
116
+
117
+ # Write two jsonl files:
118
+ # 1) Probas for all of C4
119
+ # 2) Probas + texts for samples predicted as tasky
120
+ tasky_commits_path = f"tasky_commits_python_{args.start}_{args.end}.jsonl"
121
+
122
+ with open(tasky_commits_path, "w") as f:
123
+ for i in range(len(preds)):
124
+ predicted_class_id = preds[i].argmax().item()
125
+ pred = model.config.id2label[predicted_class_id]
126
+ tasky_proba = torch.softmax(preds[i], dim=-1)[-1].item()
127
+
128
+ f.write(
129
+ json.dumps(
130
+ {
131
+ "commit": commits[i],
132
+ "message": texts[i],
133
+ "proba": tasky_proba,
134
+ }
135
+ )
136
+ + "\n"
137
+ )
138
+
inference.sh ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH --job-name=inference
3
+ #SBATCH --nodes=1
4
+ #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
5
+ #SBATCH --cpus-per-task=8 # number of cores per tasks
6
+ #SBATCH --hint=nomultithread # we get physical cores not logical
7
+ #SBATCH --gres=gpu:1 # number of gpus
8
+ #SBATCH --time 12:00:00 # maximum execution time (HH:MM:SS)
9
+ #SBATCH --output=%x-%j.out # output file name
10
+ #SBATCH --account=ajs@v100
11
+
12
+ set -x -e
13
+
14
+ source $six_ALL_CCFRWORK/start-tr13f-6B3-ml-t0
15
+ echo "START TIME: $(date)"
16
+
17
+ NUM_SAMPLES=3290900
18
+ NUM_SAMPLES_PER_JOB=32909
19
+ #NUM_SAMPLES_PER_JOB=128
20
+
21
+ TASK_START=$((($SLURM_ARRAY_TASK_ID) * $NUM_SAMPLES_PER_JOB))
22
+ TASK_END=$(($TASK_START + $NUM_SAMPLES_PER_JOB))
23
+
24
+ echo "Starting task $SLURM_ARRAY_TASK_ID with start $TASK_START and end $TASK_END"
25
+ python inference.py --start $TASK_START --end $TASK_END
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