os private dataset

model/supervised_finetuning/custom_datasets/__init__.py

@@ -1,7 +1,12 @@
 """
     High level functions for model training
 """
-from custom_datasets.prompt_dialogue import InstructionTuning, PrivateInstructionTuning, PromptGeneratedDataset
+from custom_datasets.prompt_dialogue import (
+    InstructionTuning,
+    OAPrivate,
+    PrivateInstructionTuning,
+    PromptGeneratedDataset,
+)
 from custom_datasets.qa_datasets import SODA, JokeExplaination, QADataset, SODADialogue, TranslatedQA, WebGPT
 from custom_datasets.summarization import SummarizationDataset
 from custom_datasets.toxic_conversation import ProsocialDialogue, ProsocialDialogueExplaination
@@ -36,6 +41,9 @@ OTHER = ["prosocial_dialogue", "explain_prosocial", "instruct_tuning", "private_
 
 
 def train_val_dataset(dataset, val_split=0.2):
+    if val_split == 0:
+        return dataset, None
+
     train_idx, val_idx = train_test_split(
         list(range(len(dataset))), test_size=val_split, random_state=666, shuffle=True
     )
@@ -85,11 +93,13 @@ def get_one_dataset(conf, dataset_name, val_split=0.2, data_path=None, **kwargs)
         dataset = PrivateInstructionTuning(data_path)
     elif dataset_name == "oa_translated":
         dataset = TranslatedQA(data_path)  # TODO make val_split lower..?
+    elif dataset_name == "oa_private":
+        dataset = OAPrivate(data_path, **kwargs)
     else:
         raise ValueError(f"Unknown dataset {dataset_name}")
 
     # if eval not already defined
     if "dataset" in locals():
-        train, eval = train_val_dataset(dataset, val_split=val_split, **kwargs)
+        train, eval = train_val_dataset(dataset, val_split=val_split)
 
     return train, eval

model/supervised_finetuning/custom_datasets/formatting.py

@@ -1,5 +1,11 @@
 QA_SPECIAL_TOKENS = {"Question": "<human>", "Answer": "<bot>", "StartPrefix": "<prefix>", "EndPrefix": "</prefix>"}
 
 
-def format_pair(pair):
-    return "{}{}{}".format(QA_SPECIAL_TOKENS["Question"], pair[0], QA_SPECIAL_TOKENS["Answer"]), pair[1]
+def format_pair(pairs):
+    assert len(pairs) % 2 == 0
+    return [
+        "{}{}{}".format(QA_SPECIAL_TOKENS["Question"], pairs[i], QA_SPECIAL_TOKENS["Answer"])
+        if i % 2 == 0
+        else pairs[i]
+        for i in range(0, len(pairs), 2)
+    ]

model/supervised_finetuning/custom_datasets/prompt_dialogue.py

@@ -12,10 +12,9 @@ from torch.utils.data import Dataset
 
 
 class OAPrivate(Dataset):
-    file = "2023-02-10_oasst_prod.jsonl"
     splits = OrderedDict(sft=0.25, reward_model=0.4, rl=0.35)  # fractions per task
 
-    def __init__(self, split="sft", data_path=".cache") -> None:
+    def __init__(self, data_path, split="sft", file="2023-02-10_oasst_prod.jsonl") -> None:
         super().__init__()
 
         total_prob = reduce(lambda prev, split: prev + split[1], self.splits.items(), 0)
@@ -56,14 +55,17 @@ class OAPrivate(Dataset):
 
             reply = random.choice(prompt["replies"])
             reply_text = reply["text"]
-            pairs.append([prompter_text, reply_text])
+
+            # only add if the reply exists
+            pairs.append(prompter_text)
+            pairs.append(reply_text)
 
             if len(reply["replies"]) == 0:
                 break
 
             prompt = random.choice(reply["replies"])
 
-        return pairs
+        return format_pair(pairs)
 
 
 class PromptGeneratedDataset(Dataset):