Upload huge base dataset (36GB)

BaseSFTPtData.py

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+# =============================================================================
+# COPYRIGHT © 2025 Konstantin Vladimirovich Grabko. ALL RIGHTS RESERVED.
+# CMS Manhattan JiRack Technology — PATENT PENDING
+#
+# This code is proprietary. 
+# Personal and non-commercial research use is allowed.
+# Any commercial use, derivative works for profit, or distribution 
+# requires a paid license and 5% royalty.
+#
+# Unauthorized commercial use is strictly prohibited.
+# Contact: grabko@cmsmanhattan.com
+# =============================================================================
+
+import torch
+import os
+from transformers import AutoTokenizer
+from tqdm import tqdm
+
+def stream_docs(file_path, delimiter="<|end_of_text|>"):
+    buffer = ""
+    with open(file_path, 'r', encoding='utf-8') as f:
+        while True:
+            chunk = f.read(1024 * 1024) # 1MB
+            if not chunk:
+                if buffer.strip(): yield buffer
+                break
+            buffer += chunk
+            while delimiter in buffer:
+                doc, buffer = buffer.split(delimiter, 1)
+                if doc.strip(): yield doc
+
+def tokenize_with_overlap(
+    input_file="jirack_base_dataset.txt",
+    #model_id="meta-llama/Llama-3.1-8B-Instruct",
+    model_id=".",
+    chunk_size=2000,
+    max_length=8192,
+    overlap_size=512,
+    output_prefix="jirack_overlap_data"
+):
+    print(f"📥 Загрузка токенизатора: {model_id}")
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    
+    # КРИТИЧЕСКИЙ ФИКС: Проверяем pad_token_id
+    if tokenizer.pad_token_id is None:
+        if tokenizer.eos_token_id is not None:
+            tokenizer.pad_token_id = tokenizer.eos_token_id
+        else:
+            tokenizer.pad_token_id = 128004 # Дефолт для Llama 3
+    
+    pad_id = tokenizer.pad_token_id
+    print(f"🛠 Используемый Pad Token ID: {pad_id}")
+
+    stride = max_length - overlap_size
+    input_ids_buffer = []
+    labels_buffer = []
+    chunk_idx = 0
+
+    def save_chunk(ids, labels, idx):
+        if not ids: return
+        filename = f"{output_prefix}_{idx}.pt"
+        torch.save({
+            "input_ids": torch.stack(ids).to(torch.int64),
+            "labels": torch.stack(labels).to(torch.int64)
+        }, filename)
+        print(f"\n💾 Сохранен чанк {idx}: {filename} ({len(ids)} строк)")
+
+    print(f"🔄 Нарезка 36GB файла. Окно: {max_length}, Нахлест: {overlap_size}")
+
+    for doc in tqdm(stream_docs(input_file), desc="Processing"):
+        try:
+            text = doc.strip()
+            if not text: continue
+
+            full_text = f"<|begin_of_text|>{text}<|end_of_text|>"
+            full_ids = tokenizer.encode(full_text, add_special_tokens=False)
+            
+            if not full_ids: continue
+
+            # Нарезаем на окна
+            windows = []
+            if len(full_ids) <= max_length:
+                windows.append(full_ids)
+            else:
+                for i in range(0, len(full_ids), stride):
+                    w = full_ids[i : i + max_length]
+                    if len(w) > 10:
+                        windows.append(w)
+
+            for w in windows:
+                ids = list(w)
+                lbs = list(w)
+
+                if len(ids) < max_length:
+                    pad_len = max_length - len(ids)
+                    # Используем проверенный pad_id
+                    ids += [pad_id] * pad_len
+                    lbs += [-100] * pad_len
+                
+                input_ids_buffer.append(torch.tensor(ids, dtype=torch.int64))
+                labels_buffer.append(torch.tensor(lbs, dtype=torch.int64))
+
+                if len(input_ids_buffer) >= chunk_size:
+                    save_chunk(input_ids_buffer, labels_buffer, chunk_idx)
+                    chunk_idx += 1
+                    input_ids_buffer, labels_buffer = [], []
+
+        except Exception as e:
+            # Теперь мы будем видеть реальную ошибку, если она осталась
+            print(f"\n⚠️ Ошибка: {e}")
+            continue
+
+    if input_ids_buffer:
+        save_chunk(input_ids_buffer, labels_buffer, chunk_idx)
+
+if __name__ == "__main__":
+    tokenize_with_overlap()