add multidata strats

src/axolotl/utils/data/shared.py

@@ -555,7 +555,7 @@ def merge_datasets(
 
     if has_iterable:
         LOG.info("Merging streaming datasets...")
-        merged_dataset = interleave_datasets(datasets, seed=cfg.seed)
+        merged_dataset = _merge_streaming_datasets(datasets, cfg)
     else:
         # If enabled, shuffle each dataset independently before merging.
         # This allows curriculum learning strategies to be applied at the dataset level.
@@ -581,3 +581,31 @@ def merge_datasets(
             LOG.debug("Not shuffling merged datasets.")
 
     return merged_dataset
+
+
+def _merge_streaming_datasets(
+    datasets: list[Dataset | IterableDataset], cfg: DictDefault
+) -> IterableDataset:
+    """Merge streaming datasets using the configured mixing strategy.
+
+    Args:
+        datasets: List of datasets to merge (at least one must be IterableDataset).
+        cfg: Configuration object containing streaming mixing settings.
+
+    Returns:
+        Merged IterableDataset.
+    """
+    # Get mixing configuration
+    strategy = cfg.get("streaming_dataset_mixing_strategy", "round_robin")
+    weights = cfg.get("streaming_mixing_weights", None)
+
+    LOG.info(f"Using streaming mixing strategy: {strategy}")
+
+    if strategy == "round_robin":
+        return interleave_datasets(datasets, seed=cfg.seed)
+    if strategy == "weighted":
+        return interleave_datasets(datasets, probabilities=weights, seed=cfg.seed)
+
+    return interleave_datasets(
+        datasets, probabilities=[1.0 / len(datasets)] * len(datasets), seed=cfg.seed
+    )

src/axolotl/utils/data/utils.py

@@ -193,15 +193,13 @@ def handle_long_seq_in_dataset(
     if hasattr(dataset, "column_names") and dataset.column_names:
         if "input_ids" not in dataset.column_names:
             LOG.warning(
-                "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
-                "expected for reward modeling."
+                "Dataset does not contain 'input_ids' column. Skip drop long seq. This "
+                "is expected for reward modeling."
             )
             return dataset
-    else:
-        # For IterableDataset, we can't check columns upfront, so skip for streaming
-        if isinstance(dataset, IterableDataset):
-            LOG.info("Skipping drop_long_seq for streaming datasets (not compatible)")
-            return dataset
+    elif isinstance(dataset, IterableDataset):
+        LOG.info("Skipping drop_long_seq for streaming datasets (not compatible)")
+        return dataset
 
     drop_long = functools.partial(
         drop_long_seq,

src/axolotl/utils/schemas/config.py

@@ -938,14 +938,12 @@ class AxolotlInputConfig(
             "description": "Whether to use streaming datasets (IterableDataset) for processing large datasets that don't fit in memory. When True, data is loaded on-demand during training without upfront preprocessing. Requires max_steps to be set. Pre-training datasets default to streaming unless explicitly set to False."
         },
     )
-
     streaming_dataset_mixing_strategy: str | None = Field(
         default="round_robin",
         json_schema_extra={
             "description": "Strategy for mixing multiple streaming datasets: 'round_robin' (equal sampling), 'weighted' (use streaming_mixing_weights), or 'random' (random sampling with equal probability)."
         },
     )
-
     streaming_mixing_weights: list[float] | None = Field(
         default=None,
         json_schema_extra={
@@ -953,13 +951,6 @@ class AxolotlInputConfig(
         },
     )
 
-    streaming_buffer_per_dataset: int | None = Field(
-        default=1000,
-        json_schema_extra={
-            "description": "Buffer size per dataset when mixing multiple streaming datasets. Higher values may improve mixing quality but use more memory."
-        },
-    )
-
     # INTERNALS - document for now, generally not set externally
     is_preprocess: bool | None = None
 

src/axolotl/utils/schemas/validation.py

@@ -1473,6 +1473,48 @@ class StreamingValidationMixin:
 
         return self
 
+    @model_validator(mode="after")
+    def check_streaming_mixing_weights(self):
+        """Validate streaming_mixing_weights configuration."""
+        strategy = getattr(self, "streaming_dataset_mixing_strategy", "round_robin")
+        weights = getattr(self, "streaming_mixing_weights", None)
+
+        # Validate strategy values
+        valid_strategies = ["round_robin", "weighted", "random"]
+        if strategy not in valid_strategies:
+            raise ValueError(
+                f"streaming_dataset_mixing_strategy must be one of {valid_strategies}, "
+                f"got '{strategy}'"
+            )
+
+        if strategy == "weighted":
+            if weights is None:
+                raise ValueError(
+                    "streaming_mixing_weights must be provided when "
+                    "streaming_dataset_mixing_strategy='weighted'"
+                )
+
+            if not isinstance(weights, list) or not all(
+                isinstance(w, (int, float)) for w in weights
+            ):
+                raise ValueError("streaming_mixing_weights must be a list of numbers")
+
+            if any(w < 0 for w in weights):
+                raise ValueError("streaming_mixing_weights must be non-negative")
+
+            if abs(sum(weights) - 1.0) > 1e-6:
+                raise ValueError(
+                    f"streaming_mixing_weights must sum to 1.0, got {sum(weights)}"
+                )
+
+        elif weights is not None and strategy != "weighted":
+            LOG.warning(
+                f"streaming_mixing_weights provided but strategy is '{strategy}'. "
+                "Weights will be ignored."
+            )
+
+        return self
+
 
 # pylint: disable=too-many-ancestors
 class ValidationMixin(