final version of Inst2Vec

my_run_mlm_no_trainer.py

@@ -43,6 +43,8 @@ from transformers import (CONFIG_MAPPING, MODEL_MAPPING, AdamW, AutoConfig,
 from my_data_collator import MyDataCollatorForPreTraining
 from process_data.utils import CURRENT_DATA_BASE
 
+HIDDEN_SIZE=256
+
 logger = logging.getLogger(__name__)
 MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@@ -187,7 +189,7 @@ def main():
     #     field="data",
     # )
     train_files = [
-        os.path.join(CURRENT_DATA_BASE, "inst.all.{}.json".format(i)) for i in range(2)
+        os.path.join(CURRENT_DATA_BASE, "inst.all.{}.json".format(i)) for i in [0,1,2,3,4,5,6]  # ,8,9,10,11,12,13]
     ]
     valid_file = "/home/ming/malware/inst2vec_bert/data/test_lm/inst.json"
     raw_datasets = load_dataset(
@@ -210,10 +212,10 @@ def main():
     # we use a much smaller BERT, config is:
     config = BertConfig(
         vocab_size=tokenizer.get_vocab_size(),
-        hidden_size=96,
+        hidden_size=HIDDEN_SIZE,
         num_hidden_layers=4,
-        num_attention_heads=12,
+        num_attention_heads=8,
-        intermediate_size=384,
+        intermediate_size=4 * HIDDEN_SIZE,
         max_position_embeddings=32,
     )
 
@@ -230,7 +232,10 @@ def main():
     def tokenize_function(examples):
         text = [tuple(sent) for sent in examples["text"]]
         encoded_inputs = {}
+        # try:
         results = tokenizer.encode_batch(text)
+        # except:
+        #     return None
         encoded_inputs["input_ids"] = [result.ids for result in results]
         encoded_inputs["token_type_ids"] = [result.type_ids for result in results]
         encoded_inputs["special_tokens_mask"] = [
@@ -253,7 +258,7 @@ def main():
         batched=True,
         num_proc=args.preprocessing_num_workers,
         remove_columns=column_names,
-        load_from_cache_file=False,
+        load_from_cache_file=True,
     )
 
     train_dataset = tokenized_datasets["train"]

process_data/check_length.py

@@ -1,5 +1,5 @@
 import os
-
+import  pdb
 from utils import ORIGINAL_DATA_BASE, read_file
 
 
@@ -7,7 +7,7 @@ def check(filename):
     sents = read_file(filename)
     result = 0
     for sent in sents:
-        result = max(result, len(sent[-1].replace("\t", " ").split()))
+        result = max(result, len(sent[:-1].replace("\t", " ").split()))
     print("The longest sentence in {} has {} words".format(filename, result))
     return result
 
@@ -15,10 +15,10 @@ def check(filename):
 def main():
     longest = 0
     # for i in range(6):
-    for i in [1]:
+    for i in range(10):
         for group in ("pos", "neg"):
             filename = os.path.join(
-                ORIGINAL_DATA_BASE, "inst.{}.{}.txt".format(i, group)
+                ORIGINAL_DATA_BASE, "inst.{}.{}.txt.clean".format(i, group)
             )
             longest = max(check(filename), longest)
     print("The longest sentence in all files has {} words.".format(longest))

process_data/clean.py

@@ -0,0 +1,21 @@
+from utils import ORIGINAL_DATA_BASE, read_file, write_file
+from tqdm import tqdm 
+import os
+
+def remove(pos_file, neg_file):
+    pos = read_file(pos_file)
+    neg = read_file(neg_file)
+    rets = []
+    for n in tqdm(neg):
+        if n in pos:
+            continue
+        rets.append(n)
+    write_file(rets, neg_file)
+
+def main():
+    pos_file = os.path.join(ORIGINAL_DATA_BASE, "inst.all.pos.txt.clean")
+    neg_file = os.path.join(ORIGINAL_DATA_BASE, "inst.all.neg.txt.clean")
+    remove(pos_file, neg_file)
+
+if __name__ == "__main__":
+    main()

process_data/convert_space_format.py

@@ -19,8 +19,8 @@ def convert(fin, fout):
 
 def main():
     # for i in range(6):
-    for i in [1]:
+    for i in range(10):
-        fin = os.path.join(ORIGINAL_DATA_BASE, "linux32_0{}xxxx.all".format(i))
+        fin = os.path.join(ORIGINAL_DATA_BASE, "win32_0{}xxxx.all".format(i))
         fout = os.path.join(ORIGINAL_DATA_BASE, "inst.{}.pos.txt".format(i))
         convert(fin, fout)
 

process_data/count_word_for_vocab.py

@@ -41,12 +41,12 @@ def counter(filename):
 def main():
     cnt = set()
     # for i in range(6):
-    for i in [1]:
+    for i in range(10):
         for group in ["pos", "neg"]:
             filename = os.path.join(
-                ORIGINAL_DATA_BASE, "inst.{}.{}.txt".format(i, group)
+                ORIGINAL_DATA_BASE, "inst.{}.{}.txt.clean".format(i, group)
             )
-            cnt += counter(filename)
+            cnt = cnt.union(counter(filename))
     print("There are {} charcters in all files".format(len(cnt)))
 
 

process_data/create_negative_examples.py

@@ -22,10 +22,12 @@ def create(pos, neg, tgt):
 
 def main():
     # for i in range(6):
-    for i in [1]:
+    for i in range(10):
-        j = (i + 1) % 6
+        j = (i + 1) % 10
-        pos = os.path.join(ORIGINAL_DATA_BASE, "linux32_0{}xxxx.all".format(i))
+        # neg = os.path.join(ORIGINAL_DATA_BASE, "linux32_0{}xxxx.all".format(j))
-        neg = os.path.join(ORIGINAL_DATA_BASE, "linux32_0{}xxxx.all".format(j))
+        # pos = os.path.join(ORIGINAL_DATA_BASE, "linux32_0{}xxxx.all".format(i))
+        pos = os.path.join(ORIGINAL_DATA_BASE, "inst.{}.pos.txt.clean".format(i))
+        neg = os.path.join(ORIGINAL_DATA_BASE, "inst.{}.pos.txt.clean".format(j))
         tgt = os.path.join(ORIGINAL_DATA_BASE, "inst.{}.neg.txt".format(i))
         create(pos, neg, tgt)
 

process_data/merge_examples_to_json.py

@@ -26,11 +26,11 @@ def write_worker(sents, json_file, index):
 def merge_to_json(pos, neg, json_file):
     sents = read_file(pos)
 
-    p = Pool(36)
+    p = Pool(6)
 
-    for i in range(64):
+    for i in range(6):
         p.apply_async(
-            write_worker, args=(sents[i * BASE : (i + 1) * BASE], json_file, i,)
+            write_worker, args=(sents[i * BASE : (i + 1) * BASE], json_file, 2+i,)
         )
     print("Waiting for all sub-processes done...")
     p.close()
@@ -55,11 +55,11 @@ def merge_to_json(pos, neg, json_file):
 
     sents = read_file(neg)
 
-    p = Pool(8)
+    p = Pool(6)
 
-    for i in range(64):
+    for i in range(6):
         p.apply_async(
-            write_worker, args=(sents[i * BASE : (i + 1) * BASE], json_file, 64 + i,)
+            write_worker, args=(sents[i * BASE : (i + 1) * BASE], json_file, 8 + i,)
         )
     print("Waiting for all sub-processes done...")
     p.close()
@@ -80,10 +80,14 @@ def merge_to_json(pos, neg, json_file):
 
 def main():
     # for i in range(6):
-    for i in [1]:
+    # for i in range(6):
-        pos = os.path.join(ORIGINAL_DATA_BASE, "inst.{}.pos.label.txt".format(i))
+    #     pos = os.path.join(ORIGINAL_DATA_BASE, "inst.{}.pos.label.txt".format(i))
-        neg = os.path.join(ORIGINAL_DATA_BASE, "inst.{}.neg.label.txt".format(i))
+    #     neg = os.path.join(ORIGINAL_DATA_BASE, "inst.{}.neg.label.txt".format(i))
-        json_file = os.path.join(CURRENT_DATA_BASE, "inst.{}.".format(i))
+    #     json_file = os.path.join(CURRENT_DATA_BASE, "inst.{}.".format(i))
+    #     merge_to_json(pos, neg, json_file)
+    pos = os.path.join(ORIGINAL_DATA_BASE, "inst.all.pos.txt.clean.label")
+    neg = os.path.join(ORIGINAL_DATA_BASE, "inst.all.neg.txt.clean.label")
+    json_file = os.path.join(CURRENT_DATA_BASE, "inst.all.")
     merge_to_json(pos, neg, json_file)
 
 

process_data/readme.md

@@ -31,7 +31,10 @@ We process the files containing negative examples similarly.
 cat inst.*.neg.txt.clean | sort -n | uniq > inst.all.neg.txt.clean
 ```
 
-Based on the `inst.all.pos.txt.clean`, we remove the lines from `inst.all.neg.txt.clean` if they also occur in `inst.all.pos.txt.clean`. This can be completed by `python clean.py`.
+Based on the `inst.all.pos.txt.clean`, we remove the lines from `inst.all.neg.txt.clean` if they also occur in `inst.all.pos.txt.clean`. This can be completed by `python clean.py`, or
+<!-- ```shell
+grep -v -f inst.all.pos.txt.clean inst.all.neg.txt.clean > inst.all.neg.txt.clean
+``` -->
 
 
 ### 5. convert to json format

process_data/utils.py

@@ -1,6 +1,7 @@
 import os
 
-ORIGINAL_DATA_BASE = "/home/ming/malware/data/elfasm_inst_pairs"
+# ORIGINAL_DATA_BASE = "/home/ming/malware/data/elfasm_inst_pairs"
+ORIGINAL_DATA_BASE = "/home/ming/malware/data/malasm_inst_pairs"
 CURRENT_DATA_BASE = "/home/ming/malware/inst2vec_bert/data/asm_bert"
 
 

train_my_tokenizer.py

@@ -99,8 +99,8 @@ def main(tokenizer_file=""):
     # dataset = load_dataset("json", data_files=json_files, field="data")
 
     text_files = [
-        os.path.join(ORIGINAL_DATA_BASE, "inst.1.{}.txt".format(group))
+        os.path.join(ORIGINAL_DATA_BASE, "inst.{}.{}.txt.clean".format(i, group))
-        for group in ["pos", "neg"]
+        for group in ["pos", "neg"] for i in range(10)
     ]
 
     dataset = []
@@ -121,4 +121,4 @@ def main(tokenizer_file=""):
 
 
 if __name__ == "__main__":
-    main(os.path.join(CURRENT_DATA_BASE, "tokenizer-inst.1.json"))
+    main(os.path.join(CURRENT_DATA_BASE, "tokenizer-inst.all.json"))