From 4fb19d67b5ddd888f47900ab73aed76733e7b8c3 Mon Sep 17 00:00:00 2001
From: Zach Nussbaum <zanussbaum@gmail.com>
Date: Sat, 22 Apr 2023 19:38:51 +0000
Subject: [PATCH] feat: tokenize texts into chunks

---
 gpt4all/index/tokenize_texts.py | 62 +++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 gpt4all/index/tokenize_texts.py

diff --git a/gpt4all/index/tokenize_texts.py b/gpt4all/index/tokenize_texts.py
new file mode 100644
index 00000000..2c3e83e2
--- /dev/null
+++ b/gpt4all/index/tokenize_texts.py
@@ -0,0 +1,62 @@
+from datasets import load_dataset
+from argparse import ArgumentParser
+from gpt4all.index.embed import Embedder
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument("--tokenized_save_path", type=str, default="tokenized")
+
+    return parser.parse_args()
+
+
+def tokenize_texts(examples, embedder):
+    split_data = {k: [] for k in examples.keys()}
+    split_data["tokenized_chunk"] = []
+    split_data["tokenized_attn_mask"] = []
+
+    keys = [k for k in examples.keys() if k != "text"]
+    for i, text in enumerate(examples["text"]):
+        tokenized_text = embedder.chunk_text(text)
+        # do we want to add sep/cls tokens to beginning and end?
+        decoded_text = embedder.tokenizer.batch_decode(
+            sequences=tokenized_text["input_ids"]
+        )
+
+        num_splits = len(tokenized_text["input_ids"])
+        split_data["id"].extend(
+            [f"{examples['id'][i]}_split_{j}" for j in range(num_splits)]
+        )
+
+        for col in keys:
+            if col != "id":
+                split_data[col].extend(
+                    [examples[col][i]] * len(tokenized_text["input_ids"])
+                )
+
+        split_data["text"].extend(decoded_text)
+        split_data["tokenized_chunk"].extend(tokenized_text["input_ids"])
+        split_data["tokenized_attn_mask"].extend(tokenized_text["attention_mask"])
+
+    return split_data
+
+
+def chunk_dataset(
+    ds_name="wikipedia",
+    version="20220301.simple",
+    sbert_model="sentence-transformers/all-MiniLM-L6-v2",
+    save_path="tokenized",
+):
+    dataset = load_dataset(ds_name, version, split="train")
+    print(len(dataset))
+    embedder = Embedder(sbert_model)
+    dataset = dataset.map(
+        lambda x: tokenize_texts(x, embedder), batched=True, num_proc=64
+    )
+
+    dataset.save_to_disk(save_path)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    chunked_dataset = chunk_dataset(save_path=args.tokenized_save_path)