Completion only fine-tuning of instruction models with collections of HF datasets

llms/mlx_lm/LORA.md

@@ -76,6 +76,14 @@ You can specify the output location with `--adapter-path`.
 You can resume fine-tuning with an existing adapter with
 `--resume-adapter-file <path_to_adapters.safetensors>`.
 
+#### Prompt Masking
+
+The default training computes a loss for every token in the sample. You can
+ignore the prompt and compute loss for just the completion by passing
+`--mask-prompt`. Note this is only supported for `chat` and `completion`
+datasets. For `chat` datasets the final message in the message list is
+considered the completion. See the [dataset section](#Data) for more details. 
+
 ### Evaluate
 
 To compute test set perplexity use:
@@ -290,11 +298,27 @@ hf_dataset:
 
 - Use `prompt_feature` and `completion_feature` to specify keys for a
   `completions` dataset. Use `text_feature` to specify the key for a `text`
-  dataset. 
+  dataset. Use `chat_feature` to specify the key for a chat dataset.
 
 - To specify the train, valid, or test splits, set the corresponding
   `{train,valid,test}_split` argument. 
 
+You can specify a list of Hugging Face datasets with a list of records each
+with the same structure as above. For example:
+
+```yaml
+hf_dataset:
+  - name: "Open-Orca/OpenOrca"
+    train_split: "train[:90%]"
+    valid_split: "train[-10%:]"
+    prompt_feature: "question"
+    completion_feature: "response"
+  - name: "trl-lib/ultrafeedback_binarized"
+    train_split: "train[:90%]"
+    valid_split: "train[-10%:]"
+    chat_feature: "chosen"
+```
+
 - Arguments specified in `config` will be passed as keyword arguments to
   [`datasets.load_dataset`](https://huggingface.co/docs/datasets/v2.20.0/en/package_reference/loading_methods#datasets.load_dataset).
 

llms/mlx_lm/lora.py

@@ -94,6 +94,14 @@ def build_parser():
         choices=["lora", "dora", "full"],
         help="Type of fine-tuning to perform: lora, dora, or full.",
     )
+
+    parser.add_argument(
+        "--mask-prompt",
+        action="store_true",
+        help="Mask the prompt in the loss when training",
+        default=False,
+    )
+
     parser.add_argument(
         "--num-layers",
         type=int,
@@ -219,6 +227,7 @@ def train_model(
             build_schedule(args.lr_schedule) if args.lr_schedule else args.learning_rate
         )
     )
+
     # Train model
     train(
         model=model,

llms/mlx_lm/tokenizer_utils.py

@@ -1,5 +1,6 @@
 import json
 from functools import partial
+from typing import List
 
 from transformers import AutoTokenizer
 
@@ -368,3 +369,8 @@ def load_tokenizer(model_path, tokenizer_config_extra={}, eos_token_ids=None):
         detokenizer_class,
         eos_token_ids=eos_token_ids,
     )
+
+
+def no_bos_or_eos(sequence: List, bos: int, eos: int) -> List:
+    removed_bos = sequence if sequence[0] != bos else sequence[1:]
+    return removed_bos[:-1] if removed_bos[-1] == eos else removed_bos

llms/mlx_lm/tuner/datasets.py

@@ -1,6 +1,8 @@
+import itertools
 import json
+import types
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Any, Dict, List, Optional
 
 from transformers import PreTrainedTokenizer
 
@@ -34,14 +36,24 @@ class ChatDataset:
     https://platform.openai.com/docs/guides/fine-tuning/example-format
     """
 
-    def __init__(self, data: List[Dict[str, str]], tokenizer: PreTrainedTokenizer):
-        self._data = [
-            tokenizer.apply_chat_template(
-                d["messages"],
-                tools=d.get("tools", None),
-            )
-            for d in data
-        ]
+    def __init__(
+        self,
+        data: List[Dict[str, str]],
+        tokenizer: PreTrainedTokenizer,
+        chat_key: str = "messages",
+        mask_prompt: bool = False,
+    ):
+        self._data = []
+        for d in data:
+            messages = d[chat_key]
+            tools = d.get("tools", None)
+            tokens = tokenizer.apply_chat_template(messages, tools=tools)
+            if mask_prompt:
+                messages = messages[:-1]
+                offset = len(tokenizer.apply_chat_template(messages, tools=tools))
+                self._data.append((tokens, offset))
+            else:
+                self._data.append(tokens)
 
     def __getitem__(self, idx: int):
         return self._data[idx]
@@ -63,16 +75,36 @@ def __init__(
         tokenizer: PreTrainedTokenizer,
         prompt_key: str,
         completion_key: str,
+        mask_prompt: bool,
     ):
-        self._data = [
-            tokenizer.apply_chat_template(
+        self._data = []
+        for d in data:
+            tokens = tokenizer.apply_chat_template(
                 [
                     {"role": "user", "content": d[prompt_key]},
                     {"role": "assistant", "content": d[completion_key]},
                 ],
             )
-            for d in data
-        ]
+            if mask_prompt:
+                offset = len(
+                    tokenizer.apply_chat_template(
+                        [{"role": "user", "content": d[prompt_key]}]
+                    )
+                )
+                self._data.append((tokens, offset))
+            else:
+                self._data.append(tokens)
+
+    def __getitem__(self, idx: int):
+        return self._data[idx]
+
+    def __len__(self):
+        return len(self._data)
+
+
+class ConcatenatedDataset:
+    def __init__(self, data: List[Any]):
+        self._data = list(itertools.chain(*data))
 
     def __getitem__(self, idx: int):
         return self._data[idx]
@@ -84,18 +116,26 @@ def __len__(self):
 def create_dataset(
     data,
     tokenizer: PreTrainedTokenizer,
-    prompt_feature: Optional[str] = None,
-    completion_feature: Optional[str] = None,
+    config,
 ):
-    prompt_feature = prompt_feature or "prompt"
-    completion_feature = completion_feature or "completion"
+    mask_prompt = getattr(config, "mask_prompt", False)
+    prompt_feature = getattr(config, "prompt_feature", "prompt")
+    text_feature = getattr(config, "text_feature", "text")
+    completion_feature = getattr(config, "completion_feature", "completion")
+    chat_feature = getattr(config, "chat_feature", "messages")
     sample = data[0]
-    if "messages" in sample:
-        return ChatDataset(data, tokenizer)
-    elif prompt_feature in sample and completion_feature in sample:
-        return CompletionsDataset(data, tokenizer, prompt_feature, completion_feature)
-    elif "text" in sample:
-        return Dataset(data, tokenizer)
+    if prompt_feature in sample and completion_feature in sample:
+        return CompletionsDataset(
+            data, tokenizer, prompt_feature, completion_feature, mask_prompt
+        )
+    elif chat_feature in sample:
+        return ChatDataset(
+            data, tokenizer, chat_key=chat_feature, mask_prompt=mask_prompt
+        )
+    elif text_feature in sample:
+        if mask_prompt:
+            raise ValueError("Prompt masking not supported for text dataset.")
+        return Dataset(data, tokenizer, text_key=text_feature)
     else:
         raise ValueError(
             "Unsupported data format, check the supported formats here:\n"
@@ -106,15 +146,14 @@ def create_dataset(
 def load_local_dataset(
     data_path: Path,
     tokenizer: PreTrainedTokenizer,
-    prompt_feature: Optional[str] = None,
-    completion_feature: Optional[str] = None,
+    config,
 ):
     def load_subset(path):
         if not path.exists():
             return []
         with open(path, "r") as fid:
             data = [json.loads(l) for l in fid]
-        return create_dataset(data, tokenizer, prompt_feature, completion_feature)
+        return create_dataset(data, tokenizer, config)
 
     names = ("train", "valid", "test")
     train, valid, test = [load_subset(data_path / f"{n}.jsonl") for n in names]
@@ -124,8 +163,7 @@ def load_subset(path):
 def load_hf_dataset(
     data_id: str,
     tokenizer: PreTrainedTokenizer,
-    prompt_feature: Optional[str] = None,
-    completion_feature: Optional[str] = None,
+    config,
 ):
     from datasets import exceptions, load_dataset
 
@@ -136,9 +174,7 @@ def load_hf_dataset(
 
         train, valid, test = [
             (
-                create_dataset(
-                    dataset[n], tokenizer, prompt_feature, completion_feature
-                )
+                create_dataset(dataset[n], tokenizer, config)
                 if n in dataset.keys()
                 else []
             )
@@ -154,61 +190,73 @@ def load_hf_dataset(
 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, config, split, hf_config):
         ds = datasets.load_dataset(
             dataset_name,
             split=split,
-            **hf_args.get("config", {}),
+            **hf_config,
         )
-        if prompt_feature and completion_feature:
-            return CompletionsDataset(ds, tokenizer, prompt_feature, completion_feature)
-        elif text_feature:
-            return Dataset(ds, tokenizer, text_key=text_feature)
+        return create_dataset(ds, tokenizer, config)
+
+    dataset_collection = args.hf_dataset
+    if isinstance(dataset_collection, dict):
+        dataset_collection = [dataset_collection]
+
+    collection = []
+    for ds in dataset_collection:
+        ds_name = ds["name"]
+        print(f"Loading Hugging Face dataset {ds_name}.")
+        ds["mask_prompt"] = getattr(args, "mask_prompt", False)
+        config = types.SimpleNamespace(**ds)
+        hf_config = ds.get("config", {})
+        if args.train:
+            train_split = ds.get("train_split", "train[:80%]")
+            valid_split = ds.get("valid_split", "train[-10%:]")
+            train = create_hf_dataset(
+                ds_name,
+                config,
+                train_split,
+                hf_config,
+            )
+            valid = create_hf_dataset(
+                ds_name,
+                config,
+                valid_split,
+                hf_config,
+            )
         else:
-            raise ValueError(
-                "Specify either a prompt and completion feature or a text "
-                "feature for the Hugging Face dataset."
+            train, valid = [], []
+
+        if args.test:
+            test_split = ds.get("test_split")
+            test = create_hf_dataset(
+                ds_name,
+                config,
+                test_split,
+                hf_config,
             )
+        else:
+            test = []
 
-    if args.train:
-        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)
-    else:
-        train, valid = [], []
-    if args.test:
-        test = create_hf_dataset(split=hf_args.get("test_split"))
-    else:
-        test = []
+        collection.append((train, valid, test))
 
-    return train, valid, test
+    if len(collection) == 1:
+        return collection[0]
+
+    # Otherwise concatenate them
+    return tuple(map(ConcatenatedDataset, zip(*collection)))
 
 
 def load_dataset(args, tokenizer: PreTrainedTokenizer):
     if getattr(args, "hf_dataset", False):
         train, valid, test = load_custom_hf_dataset(args, tokenizer)
     else:
         data_path = Path(args.data)
-
-        prompt_feature = getattr(args, "prompt_feature", None)
-        completion_feature = getattr(args, "completion_feature", None)
         if data_path.exists():
-            train, valid, test = load_local_dataset(
-                data_path, tokenizer, prompt_feature, completion_feature
-            )
+            train, valid, test = load_local_dataset(data_path, tokenizer, args)
         else:
             print(f"Loading Hugging Face dataset {args.data}.")
-            train, valid, test = load_hf_dataset(
-                args.data, tokenizer, prompt_feature, completion_feature
-            )
+            train, valid, test = load_hf_dataset(args.data, tokenizer, args)
 
     if args.train and len(train) == 0:
         raise ValueError(