refactor: clean up prep index

gpt4all/index/prep_index_for_train.py

@@ -9,67 +9,86 @@ from pyarrow import compute as pc
 from argparse import ArgumentParser
 from gpt4all.utils.read import read_config
 from gpt4all.index.embed_texts import embed_texts
+from tqdm import tqdm
 
-SPLIT = 'train'
 CHUNK_SIZE = 1024
-K = 5
-
-
-if __name__ == "__main__":
-
-    dist.init_process_group("nccl")
 
+def parse_args():
     parser = ArgumentParser()
     parser.add_argument("--config", type=str, default="config.yaml")
+    parser.add_argument("--split", type=str, default="train")
+    parser.add_argument("--k", type=int, default=5)
 
-    args = parser.parse_args()
+    return parser.parse_args()
 
+    
+def prep_index():
+    args = parse_args()
     config = read_config(args.config)
-
-    #load index
     index = hnswlib.Index(space=config['index_space'], dim=config['index_dim'])
+    print("loading index")
     index.load_index(config['index_path'])
 
-    #load query dataset
+    # load query dataset
     ds_path = config['dataset_path']
 
-    #load retrieval dataset
-    retrieval_dataset = Dataset.load_from_disk(config['index_database'])
+    # load retrieval dataset
+    print("loading retrieval dataset")
+    print(config["index_database"])
+    if os.path.exists(config['index_database']):
+        retrieval_dataset = Dataset.load_from_disk(config['index_database'])
+    else:
+        retrieval_dataset = load_dataset(config['index_database'], split=args.split)
 
-    #vectorize queries
-    query_vector_path =  f"{ds_path}_queries_embedded/{ds_path}_embedded_{SPLIT}"
+    # vectorize queries
+    query_vector_path =  f"{ds_path}_queries_embedded/{ds_path}_embedded_{args.split}"
     if not os.path.exists(query_vector_path):
         print('Embedding dataset...')
         ds = embed_texts(ds_path,
                          config['batch_size'],
                          embed_on=config['query_embedding_field'],
                          save_to_disk=False,
-                         split=SPLIT)
+                         split=args.split)
         ds.save_to_disk(query_vector_path)
     else:
         print('Found cached embedding dataset!')
         ds = Dataset.load_from_disk(query_vector_path)
 
     #build training dataset
-    train_dataset = load_dataset(ds_path, split=SPLIT)
+    train_dataset = load_dataset(ds_path, split=args.split)
 
     #search the index for each query
     neighbor_embs_column = []
     neighbor_ids_column = []
-    for chunk_start in range(0, len(ds), CHUNK_SIZE):
+    for chunk_start in tqdm(range(0, len(ds), CHUNK_SIZE)):
         chunk_end = chunk_start + CHUNK_SIZE
         chunk = ds[chunk_start:chunk_end]
         query_vectors = np.array(chunk['embedding'])
-        neighbor_ids, _ = index.knn_query(query_vectors, k=K, num_threads=-1)
+        neighbor_ids, _ = index.knn_query(query_vectors, k=args.k, num_threads=-1) # neighbor ids is of shape [n_queries, n_neighbors]
         value_set = pa.array([str(e) for e in neighbor_ids.flatten()])
-        #TODO @nussy should be id
-        neighbor_objs = retrieval_dataset._data.filter(pc.is_in(retrieval_dataset._data['index'], value_set))
-        neighbor_ids_column.extend(neighbor_objs['index']) #TODO @nussy should be id
-        neighbor_embs_column.extend(neighbor_objs['embedding'])
+        neighbor_objs = retrieval_dataset._data.filter(pc.is_in(retrieval_dataset._data['id'], value_set))
 
-    #import pdb;pdb.set_trace()
+        # build mapping between indices and embeddings
+        neighbor_id_list = neighbor_objs['id']
+        emb_list = neighbor_objs['embedding']
+        idx_to_embedding = {idx.as_py(): emb_list[i] for i, idx in enumerate(neighbor_id_list)}
+
+        neighbor_embs = []
+        for cur_neighbor_ids in neighbor_ids:
+            cur_embs = [idx_to_embedding[id].as_py() for id in cur_neighbor_ids]
+            neighbor_embs.append(cur_embs)
+
+        neighbor_embs_column.extend(neighbor_embs)
+        neighbor_ids_column.extend(neighbor_ids)
+
+    print("adding neighbor ids")
     train_dataset = train_dataset.add_column('neighbor_ids', neighbor_ids_column)
+    print("adding neighbor embeddings")
     train_dataset = train_dataset.add_column('neighbor_embeddings', neighbor_embs_column)
 
-    supplemented_dataset_path = f"{ds_path}_supplemented_{SPLIT}/"
+    supplemented_dataset_path = f"{ds_path}_supplemented_{args.split}/"
     train_dataset.save_to_disk(supplemented_dataset_path)
+
+
+if __name__ == "__main__":
+    prep_index()