Generalize HF datasets to a collection of HF dataasets via datasets, adds support for custom chat HF datasets (#1088), and fixes (#1087)

llms/mlx_lm/tuner/datasets.py

@@ -1,6 +1,6 @@
 import json
 from pathlib import Path
-from typing import Dict, List
+from typing import Dict, List, Union
 
 from transformers import PreTrainedTokenizer
 
@@ -29,12 +29,18 @@ class ChatDataset(Dataset):
     https://platform.openai.com/docs/guides/fine-tuning/example-format
     """
 
-    def __init__(self, data: List[Dict[str, str]], tokenizer: PreTrainedTokenizer):
+    def __init__(
+        self,
+        data: List[Dict[str, str]],
+        tokenizer: PreTrainedTokenizer,
+        chat_key: str = "messages",
+    ):
         super().__init__(data)
         self._tokenizer = tokenizer
+        self._chat_key = chat_key
 
     def __getitem__(self, idx: int):
-        messages = self._data[idx]["messages"]
+        messages = self._data[idx][self._chat_key]
         text = self._tokenizer.apply_chat_template(
             messages,
             tools=self._data[idx].get("tools", None),
@@ -76,6 +82,29 @@ class CompletionsDataset(Dataset):
         return text
 
 
+class CompletionsDatasetCollection:
+    def __init__(self, data: List[Union[ChatDataset, CompletionsDataset]]):
+        self.collection = data
+
+    def __getitem__(self, idx: int):
+        item = next(self.collection)
+
+        curr_idx = idx
+
+        while True:
+            try:
+                if (curr_idx + 1) < len(item):
+                    return item[curr_idx]
+                else:
+                    curr_idx -= len(item)
+                    item = next(self.collection)
+            except StopIteration:
+                raise IndexError(idx)
+
+    def __len__(self):
+        return sum(map(len, self.collection))
+
+
 def create_dataset(data, tokenizer: PreTrainedTokenizer = None):
     sample = data[0]
 
@@ -127,14 +156,14 @@ def load_hf_dataset(data_id: str, tokenizer: PreTrainedTokenizer):
 def load_custom_hf_dataset(args, tokenizer: PreTrainedTokenizer):
     import datasets
 
-    hf_args = args.hf_dataset
-    dataset_name = hf_args["name"]
-    print(f"Loading Hugging Face dataset {dataset_name}.")
-    text_feature = hf_args.get("text_feature")
-    prompt_feature = hf_args.get("prompt_feature")
-    completion_feature = hf_args.get("completion_feature")
-
-    def create_hf_dataset(split: str = None):
+    def create_hf_dataset(
+        dataset_name: Union[None, str],
+        text_feature: Union[None, str],
+        prompt_feature: Union[None, str],
+        completion_feature: Union[None, str],
+        chat_feature: Union[None, str],
+        split: str = None,
+    ):
         ds = datasets.load_dataset(
             dataset_name,
             split=split,
@@ -142,25 +171,93 @@ def load_custom_hf_dataset(args, tokenizer: PreTrainedTokenizer):
         )
         if prompt_feature and completion_feature:
             return CompletionsDataset(ds, tokenizer, prompt_feature, completion_feature)
+        elif chat_feature:
+            return ChatDataset(ds, tokenizer, chat_key=chat_feature)
         elif text_feature:
-            return Dataset(train_ds, text_key=text_feature)
+            return Dataset(ds, text_key=text_feature)
         else:
             raise ValueError(
                 "Specify either a prompt and completion feature or a text "
                 "feature for the Hugging Face dataset."
             )
 
-    if args.train:
+    def get_hf_custom_features(hf_args):
+        return (
+            hf_args.get("text_feature"),
+            hf_args.get("prompt_feature"),
+            hf_args.get("completion_feature"),
+            hf_args.get("chat_feature"),
+        )
+
+    def get_train_and_valid_splits(hf_args, ds_name):
         train_split = hf_args.get("train_split", "train[:80%]")
         valid_split = hf_args.get("valid_split", "train[-10%:]")
-        train = create_hf_dataset(split=train_split)
-        valid = create_hf_dataset(split=valid_split)
+        text_f, prompt_f, completion_f, chat_f = get_hf_custom_features(hf_args)
+        train = create_hf_dataset(
+            ds_name, text_f, prompt_f, completion_f, chat_f, split=train_split
+        )
+        valid = create_hf_dataset(
+            ds_name, text_f, prompt_f, completion_f, chat_f, split=valid_split
+        )
+        return train, valid
+
+    if args.datasets:
+        dataset_collection = args.hf_datasets
+        train_collection = []
+        valid_collection = []
+        test_collection = []
+        for ds in dataset_collection:
+            hf_args = ds["hf_dataset"]
+            dataset_name = hf_args["name"]
+            print(f"Loading Hugging Face dataset {dataset_name}.")
+            text_feature, prompt_feature, completion_feature, chat_f = (
+                get_hf_custom_features(hf_args)
+            )
+            if args.train:
+                train, valid = get_train_and_valid_splits(hf_args, dataset_name)
+            else:
+                train, valid = [], []
+            if args.test:
+                test = create_hf_dataset(
+                    dataset_name,
+                    text_feature,
+                    prompt_feature,
+                    completion_feature,
+                    chat_f,
+                    split=hf_args.get("test_split"),
+                )
+            else:
+                test = []
+            train_collection.append(train)
+            valid_collection.append(valid)
+            test_collection.append(test)
+        return (
+            CompletionsDatasetCollection(train_collection),
+            CompletionsDatasetCollection(valid_collection),
+            CompletionsDatasetCollection(test_collection),
+        )
     else:
-        train, valid = [], []
-    if args.test:
-        test = create_hf_dataset(split=hf_args.get("test_split"))
-    else:
-        test = []
+        hf_args = args.hf_dataset
+        dataset_name = hf_args["name"]
+        print(f"Loading Hugging Face dataset {dataset_name}.")
+        text_feature, prompt_feature, completion_feature, chat_feature = (
+            get_hf_custom_features(hf_args)
+        )
+        if args.train:
+            train, valid = get_train_and_valid_splits(hf_args, dataset_name)
+        else:
+            train, valid = [], []
+        if args.test:
+            test = create_hf_dataset(
+                dataset_name,
+                text_feature,
+                prompt_feature,
+                completion_feature,
+                chat_feature,
+                split=hf_args.get("test_split"),
+            )
+        else:
+            test = []
 
     return train, valid, test