Upload datasetChunker.py

datasetChunker.py

@@ -0,0 +1,99 @@
+from transformers import AutoTokenizer
+import jsonlines
+import random
+import os
+
+tokenizer = AutoTokenizer.from_pretrained("NilanE/tinyllama-relora-merge")
+
+max_seq_len = 2048 # max context length
+
+prompt = "Translate this from Japanese to English:\n### JAPANESE:  \n### ENGLISH: </s>" # insert SFT prompt to add to token count
+
+input_file_path = "dataset-parallel-complete.jsonl"
+
+output_file_path = input_file_path.split('.')[0] + "-chunked." + input_file_path.split('.')[1]
+promptTokens = len(tokenizer.tokenize(prompt))
+
+def load_jsonl(file_path):
+    data = []
+    with jsonlines.open(file_path) as reader:
+        for entry in reader:
+            source = entry['src'].replace('</s>', '').strip()
+            target = entry['trg'].replace('</s>', '').strip()
+            data.append([source, target])
+    return data
+
+def save_jsonl(file_path, data):
+    with jsonlines.open(file_path, 'w') as writer:
+        writer.write_all(data)
+                    
+chunks = []
+
+data = load_jsonl(input_file_path)
+
+#tolerance
+max_seq_len -= 10
+
+skippedDocs = 0
+
+for doc in data:
+
+    src_lines = doc[0].split('\n')
+    trg_lines = doc[1].split('\n')
+
+    out_src = []
+    out_trg = []
+    tokenCount = 0
+    lastTokenCount = 0
+    longLines = 0
+
+    try:
+        for x in range(len(src_lines)):
+            out_src.append(src_lines[x])
+            out_trg.append(trg_lines[x])
+            out_src_string = "\n".join(out_src)
+            trg_src_string = "\n".join(out_trg)
+            tokenCount = len(tokenizer.tokenize(out_src_string.strip() + trg_src_string.strip())) + promptTokens
+            if tokenCount-lastTokenCount < max_seq_len-1: # avoid lines > max line length    
+                if tokenCount > max_seq_len-1:
+                    src_end = out_src.pop()
+                    trg_end = out_trg.pop()
+                    out_src_string = "\n".join(out_src)
+                    trg_src_string = "\n".join(out_trg)
+                    data = {
+                        'src' : out_src_string.strip(),
+                        'trg' : trg_src_string.strip()
+                    }
+                    chunks.append(data)
+                    out_src = [src_end]
+                    out_trg = [trg_end]
+                elif x+1 == len(src_lines): #and len(out_src) > 2:
+                    data = {
+                        'src' : out_src_string.strip(),
+                        'trg' : trg_src_string.strip()
+                    }
+                    chunks.append(data)
+            else:
+                # remove offending line > max_seq_len
+                out_src.pop()
+                out_trg.pop()
+                out_src_string = "\n".join(out_src)
+                trg_src_string = "\n".join(out_trg)
+                tokenCount = len(tokenizer.tokenize(prompt + out_src_string.strip() + trg_src_string.strip()))   
+                longLines += 1
+
+            lastTokenCount = tokenCount
+    except:
+        skippedDocs += 1
+        
+
+random.shuffle(chunks)
+
+print(f"LINES LONGER THAN MAX SEQUENCE LENTH: {longLines}")
+print(f"SKIPPED DOCS: {skippedDocs}")
+
+# Save the randomized data to a new JSONL file
+if os.path.exists(output_file_path):
+    os.remove(output_file_path)
+save_jsonl(output_file_path, chunks)
+