feat: Baseten contrib third-party-dataset support

modelopt/torch/utils/dataset_utils.py

@@ -110,77 +110,148 @@
 ]
 
 
+def _normalize_splits(split: str | list[str]) -> list[str]:
+    """Ensure split is always a list."""
+    return [split] if isinstance(split, str) else list(split)
+
+
+def _auto_preprocess_sample(
+    sample: dict,
+    dataset_name: str,
+    tokenizer: "PreTrainedTokenizerBase | None" = None,
+) -> str:
+    """Auto-detect dataset format and preprocess a single sample based on column conventions.
+
+    Column detection order (first match wins):
+        1. ``messages`` / ``conversations`` -> ``tokenizer.apply_chat_template`` (with ``tools`` if present)
+        2. ``prompt`` (+ optional ``completion`` / ``response`` / ``output``) -> concatenate
+        3. ``text`` -> use as-is
+        4. ``input`` (+ optional ``output``) -> concatenate
+
+    Raises:
+        ValueError: If the tokenizer is missing/incompatible for chat-format datasets,
+            or if no recognized column is found.
+    """
+    chat_key = next((k for k in ("messages", "conversations") if sample.get(k)), None)
+    if chat_key is not None:
+        if tokenizer is None or not hasattr(tokenizer, "apply_chat_template"):
+            raise ValueError(
+                f"Dataset '{dataset_name}' has a '{chat_key}' column but no tokenizer with "
+                "apply_chat_template was provided."
+            )
+        kwargs: dict[str, Any] = {}
+        tools = sample.get("tools")
+        if tools:
+            kwargs["tools"] = tools
+        return tokenizer.apply_chat_template(sample[chat_key], tokenize=False, **kwargs)
+
+    if "prompt" in sample:
+        parts = [sample["prompt"]]
+        parts.extend(sample[k] for k in ("completion", "response", "output") if sample.get(k))
+        return "\n".join(parts)
+
+    if "text" in sample:
+        return sample["text"]
+
+    if "input" in sample:
+        parts = [sample["input"]]
+        if sample.get("output"):
+            parts.append(sample["output"])
+        return "\n".join(parts)
+
+    raise ValueError(
+        f"Cannot auto-detect format for dataset '{dataset_name}'. "
+        f"Found columns: {list(sample.keys())}. "
+        "Expected one of: 'messages', 'conversations', 'prompt', 'text', or 'input'."
+    )
+
+
 def get_dataset_samples(
     dataset_name: str,
     num_samples: int,
     *,
     apply_chat_template: bool = False,
     tokenizer: "PreTrainedTokenizerBase | None" = None,
+    split: str | list[str] | None = None,
 ) -> list[str]:
-    """Load a portion of train dataset with the dataset name and a given size.
+    """Load a portion of a dataset with the dataset name and a given size.
+
+    Supports both registered datasets (in ``SUPPORTED_DATASET_CONFIG``) and arbitrary
+    HuggingFace datasets.  Unregistered datasets are auto-detected by column names:
+    ``messages``/``conversations`` (chat), ``prompt``, ``text``, or ``input``.
 
     Args:
-        dataset_name: Name of the dataset to load.
+        dataset_name: Name or HuggingFace path of the dataset to load.
         num_samples: Number of samples to load from the dataset.
-        apply_chat_template: Whether to apply the chat template to the samples (if supported by the dataset).
+        apply_chat_template: Whether to apply the chat template to the samples
+            (if supported by the dataset).  For unregistered datasets with a
+            ``messages`` column, chat template is always applied regardless of
+            this flag.
         tokenizer: Tokenizer to use for applying the chat template to the samples.
             No tokenization is done and plain text is still returned.
+        split: Override the split(s) to load.  Accepts a single split name or a list.
+            If ``None``, uses the splits defined in ``SUPPORTED_DATASET_CONFIG`` for
+            registered datasets, or ``["train"]`` for unregistered datasets.
 
     Returns:
         Samples: The list of samples.
     """
-    # Load the dataset
-    if dataset_name not in SUPPORTED_DATASET_CONFIG:
-        raise NotImplementedError(
-            f"dataset {dataset_name} is not supported. Please use one of the following:"
-            f" {get_supported_datasets()}."
-        )
-
     from datasets import load_dataset
 
-    dataset_config = SUPPORTED_DATASET_CONFIG[dataset_name]
-    if apply_chat_template:
-        if "chat_key" not in dataset_config:
-            warn(
-                f"Dataset {dataset_name} does not support chat template. Chat template will not be applied."
-            )
-        elif tokenizer is None:
-            raise ValueError("Tokenizer is required when applying chat template.")
-        print(f"Applying chat template to dataset {dataset_name}")
-
-    # It's unfortunate that the load_dataset function does not support split a list while streaming.
-    # So we need to load the dataset for each split.
-    config = dataset_config["config"].copy()
-    splits = config.pop("split", [None])
-    dataset_splits = [
-        load_dataset(
-            streaming=True,
-            **config,
-            split=split,
-        )
-        for split in splits
-    ]
-
-    # Split the samples evenly across the splits
-    # For streaming datasets, there is no reliable way to get the number of samples in each split
-    # other than loading the entire dataset. So, we just use the same number of samples for each split.
-    num_samples_splits = [num_samples // len(dataset_splits) for _ in dataset_splits]
-    num_samples_splits[-1] += num_samples - sum(num_samples_splits)
-    samples = []
-    for dataset, num_samples_split in zip(dataset_splits, num_samples_splits):
-        for i, sample in enumerate(dataset):
-            if i >= num_samples_split:
-                break
+    is_registered = dataset_name in SUPPORTED_DATASET_CONFIG
+
+    if is_registered:
+        dataset_config = SUPPORTED_DATASET_CONFIG[dataset_name]
+        config = dataset_config["config"].copy()
+        splits = _normalize_splits(split) if split is not None else config.pop("split", [None])
+        if split is not None:
+            config.pop("split", None)
+
+        if apply_chat_template:
+            if "chat_key" not in dataset_config:
+                warn(
+                    f"Dataset {dataset_name} does not support chat template."
+                    " Chat template will not be applied."
+                )
+            elif tokenizer is None:
+                raise ValueError("Tokenizer is required when applying chat template.")
 
-            # Apply preprocess function to the sample
+        def _preprocess(sample: dict) -> str:
             if apply_chat_template and "chat_key" in dataset_config:
-                sample = tokenizer.apply_chat_template(  # type: ignore[union-attr]
-                    sample[dataset_config["chat_key"]], tokenize=False
+                kwargs: dict[str, Any] = {}
+                tools = sample.get("tools")
+                if tools:
+                    kwargs["tools"] = tools
+                return tokenizer.apply_chat_template(  # type: ignore[union-attr]
+                    sample[dataset_config["chat_key"]], tokenize=False, **kwargs
                 )
-            else:
-                sample = dataset_config["preprocess"](sample)
-            if sample != "":  # wikitext has some empty samples
-                samples.append(sample)
+            return dataset_config["preprocess"](sample)
+
+    else:
+        warn(
+            f"Dataset '{dataset_name}' is not in SUPPORTED_DATASET_CONFIG. "
+            "Auto-detecting format from column names."
+        )
+        config = {"path": dataset_name}
+        splits = _normalize_splits(split) if split is not None else ["train"]
+
+        def _preprocess(sample: dict) -> str:
+            return _auto_preprocess_sample(sample, dataset_name, tokenizer)
+
+    # load_dataset does not support a list of splits while streaming, so load each separately.
+    dataset_splits = [load_dataset(streaming=True, **config, split=s) for s in splits]
+
+    num_per_split = [num_samples // len(dataset_splits)] * len(dataset_splits)
+    num_per_split[-1] += num_samples - sum(num_per_split)
+
+    samples: list[str] = []
+    for dataset, n in zip(dataset_splits, num_per_split):
+        for i, sample in enumerate(dataset):
+            if i >= n:
+                break
+            text = _preprocess(sample)
+            if text:
+                samples.append(text)
 
     return samples
 
@@ -208,20 +279,23 @@ def get_dataset_dataloader(
     max_sample_length: int = 512,
     device: torch.device | None = None,
     include_labels: bool = False,
+    apply_chat_template: bool = False,
 ) -> DataLoader:
-    """Get a dataloader with the dataset name and toknizer of the target model.
+    """Get a dataloader with the dataset name and tokenizer of the target model.
 
     Args:
         dataset_name: Name of the dataset to load.
-        tokenizer: Instancne of Hugginface tokenizer.
+        tokenizer: Instance of HuggingFace tokenizer.
         batch_size: Batch size of the returned dataloader.
         num_samples: Number of samples from the dataset.
         max_sample_length: Maximum length of a sample.
         device: Target device for the returned dataloader.
         include_labels: Whether to include labels in the dataloader.
+        apply_chat_template: Whether to apply the chat template to the samples
+            (if supported by the dataset).
 
     Returns:
-        A instance of dataloader.
+        An instance of dataloader.
     """
     assert tokenizer is not None, "Please provide a tokenizer."
     # batch_encode_plus will modify the tokenizer in place, so we need to clone it.
@@ -244,7 +318,9 @@ def get_dataset_dataloader(
 
     all_samples = []
     for ds_name, num_sample in zip(dataset_name, num_samples):
-        samples = get_dataset_samples(ds_name, num_sample)
+        samples = get_dataset_samples(
+            ds_name, num_sample, apply_chat_template=apply_chat_template, tokenizer=tokenizer
+        )
         all_samples.extend(samples)
 
     batch_encoded = tokenizer.batch_encode_plus(