synthetic datasets for benchmarking and testing (#3518) [skip ci]

* synthetic datasets for benchmarking and testing

* fix synthetic dataset parse from config and add tests

* use type=_synthetic

src/axolotl/prompt_strategies/_synthetic.py

@@ -0,0 +1,96 @@
+"""
+Synthetic dataset generator for benchmarking and testing.
+
+Generates datasets with configurable sequence length, dataset size, and token ID ranges.
+Useful for benchmarking memory usage and speed by sequence length, and for validating
+weighted dataset mixes.
+
+YAML configuration example:
+
+    datasets:
+      - path: synthetic
+        type: _synthetic
+        length: 1000
+        sequence_length: 2048
+        min_input_id: 100
+        max_input_id: 32000
+        seed: 42
+"""
+
+from typing import Any, Dict, Optional
+
+import numpy as np
+from datasets import Dataset
+
+from axolotl.prompt_tokenizers import DatasetWrappingStrategy
+from axolotl.utils.logging import get_logger
+
+LOG = get_logger(__name__)
+
+
+class SyntheticDatasetStrategy(DatasetWrappingStrategy):
+    """Strategy that generates synthetic tokenized data, ignoring the source dataset."""
+
+    def __init__(
+        self,
+        sequence_length: int = 2048,
+        length: int = 1000,
+        min_input_id: int = 100,
+        max_input_id: int = 32000,
+        seed: Optional[int] = None,
+    ):
+        self.sequence_length = sequence_length
+        self.length = length
+        self.min_input_id = min_input_id
+        self.max_input_id = max_input_id
+        self.seed = seed
+
+    def wrap_dataset(
+        self,
+        dataset,
+        process_count: int | None = None,
+        keep_in_memory: bool | None = False,
+        **kwargs,
+    ) -> Dataset:
+        LOG.info(
+            f"Generating synthetic dataset: {self.length} samples, "
+            f"sequence_length={self.sequence_length}, "
+            f"input_id_range=[{self.min_input_id}, {self.max_input_id})"
+        )
+
+        rng = np.random.default_rng(self.seed)
+        input_ids = rng.integers(
+            low=self.min_input_id,
+            high=self.max_input_id,
+            size=(self.length, self.sequence_length),
+        ).tolist()
+
+        attention_mask = [[1] * self.sequence_length] * self.length
+        # labels == input_ids means we train on all tokens
+        labels = [row[:] for row in input_ids]
+
+        return Dataset.from_dict(
+            {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+                "labels": labels,
+            }
+        )
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    ds_cfg = ds_cfg or {}
+
+    sequence_length = ds_cfg.get("sequence_length", cfg.sequence_len)
+    length = ds_cfg.get("length", 1000)
+    min_input_id = ds_cfg.get("min_input_id", 100)
+    max_input_id = ds_cfg.get("max_input_id", tokenizer.vocab_size)
+    seed = ds_cfg.get("seed", None)
+
+    return SyntheticDatasetStrategy(
+        sequence_length=sequence_length,
+        length=length,
+        min_input_id=min_input_id,
+        max_input_id=max_input_id,
+        seed=seed,
+    )

src/axolotl/utils/config/__init__.py

@@ -22,7 +22,12 @@ from axolotl.utils.schemas.config import (
     AxolotlConfigWCapabilities as AxolotlConfigWCapabilitiesBase,
     AxolotlInputConfig as AxolotlInputConfigBase,
 )
-from axolotl.utils.schemas.datasets import DPODataset, KTODataset, SFTDataset
+from axolotl.utils.schemas.datasets import (
+    DPODataset,
+    KTODataset,
+    SFTDataset,
+    SyntheticDataset,
+)
 
 LOG = get_logger(__name__)
 
@@ -308,6 +313,14 @@ def validate_config(
                 cfg["datasets"][idx] = DPODataset(**ds_cfg)
             elif cfg.get("rl") == "kto" and not isinstance(ds_cfg, KTODataset):
                 cfg["datasets"][idx] = KTODataset(**dict(ds_cfg))
+            elif (
+                ds_cfg.get("type")
+                if isinstance(ds_cfg, dict)
+                else getattr(ds_cfg, "type", None)
+            ) == "_synthetic" and not isinstance(ds_cfg, SyntheticDataset):
+                cfg["datasets"][idx] = SyntheticDataset(
+                    **(ds_cfg if isinstance(ds_cfg, dict) else dict(ds_cfg))
+                )
             elif not isinstance(ds_cfg, SFTDataset):
                 cfg["datasets"][idx] = SFTDataset(**dict(ds_cfg))
 

src/axolotl/utils/data/sft.py

@@ -376,10 +376,14 @@ def _load_and_process_single_dataset(
     streaming: bool = False,
 ) -> tuple[Dataset | IterableDataset, Prompter | None]:
     """Load and process a single dataset based on the passed config."""
-    # Load the dataset
-    dataset = load_dataset_with_config(
-        dataset_config, cfg.hf_use_auth_token, streaming=streaming
-    )
+    # For synthetic datasets, create a minimal placeholder instead of loading from path
+    if dataset_config.type == "_synthetic":
+        dataset = Dataset.from_dict({"text": [""]})
+    else:
+        # Load the dataset
+        dataset = load_dataset_with_config(
+            dataset_config, cfg.hf_use_auth_token, streaming=streaming
+        )
 
     # Parse dataset type
     d_base_type, d_prompt_style = _parse_dataset_type(dataset_config.type)

src/axolotl/utils/schemas/config.py

@@ -22,6 +22,7 @@ from axolotl.utils.schemas.datasets import (
     PretrainingDataset,
     SFTDataset,
     StepwiseSupervisedDataset,
+    SyntheticDataset,
 )
 from axolotl.utils.schemas.deprecated import DeprecatedParameters, RemappedParameters
 from axolotl.utils.schemas.dynamic_checkpoint import DynamicCheckpointConfig
@@ -185,7 +186,13 @@ class AxolotlInputConfig(
 
     datasets: (
         Annotated[
-            list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset],
+            list[
+                SFTDataset
+                | DPODataset
+                | KTODataset
+                | StepwiseSupervisedDataset
+                | SyntheticDataset
+            ],
             MinLen(1),
         ]
         | None
@@ -198,7 +205,13 @@ class AxolotlInputConfig(
 
     test_datasets: (
         Annotated[
-            list[SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset],
+            list[
+                SFTDataset
+                | DPODataset
+                | KTODataset
+                | StepwiseSupervisedDataset
+                | SyntheticDataset
+            ],
             MinLen(1),
         ]
         | None

src/axolotl/utils/schemas/datasets.py

@@ -296,4 +296,42 @@ class KTODataset(BaseModel):
     revision: str | None = None
 
 
-DatasetConfig = SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset
+class SyntheticDataset(BaseModel):
+    """Synthetic dataset configuration for benchmarking and testing.
+
+    Generates datasets with configurable sequence length, dataset size, and token ID
+    ranges. Useful for benchmarking memory usage and speed by sequence length, and for
+    validating weighted dataset mixes.
+    """
+
+    path: Literal["synthetic"] = "synthetic"
+    type: Literal["_synthetic"] = "_synthetic"
+    length: int = Field(
+        default=1000,
+        json_schema_extra={"description": "Number of rows to generate"},
+    )
+    sequence_length: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Sequence length per row (defaults to sequence_len from config)"
+        },
+    )
+    min_input_id: int = Field(
+        default=100,
+        json_schema_extra={"description": "Minimum token ID for generation"},
+    )
+    max_input_id: int | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Maximum token ID for generation (defaults to tokenizer vocab_size)"
+        },
+    )
+    seed: int | None = Field(
+        default=None,
+        json_schema_extra={"description": "Random seed for reproducibility"},
+    )
+
+
+DatasetConfig = (
+    SFTDataset | DPODataset | KTODataset | StepwiseSupervisedDataset | SyntheticDataset
+)

tests/patched/test_validation.py

@@ -13,6 +13,7 @@ from axolotl.utils.config import validate_config
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.schemas.config import AxolotlConfigWCapabilities
+from axolotl.utils.schemas.datasets import SFTDataset
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
 warnings.filterwarnings("error")
@@ -1731,3 +1732,52 @@ class TestDataloaderValidation(BaseValidation):
         assert new_cfg.dataloader_num_workers == 8
         assert new_cfg.dataloader_pin_memory is True
         assert new_cfg.dataloader_prefetch_factor == 256
+
+
+class TestSyntheticDatasetValidation(BaseValidation):
+    """
+    Tests for synthetic dataset config validation
+    """
+
+    @staticmethod
+    def _make_cfg(minimal_cfg, datasets):
+        raw = dict(minimal_cfg)
+        raw["datasets"] = datasets
+        return DictDefault(raw)
+
+    def test_synthetic_dict_config_validates(self, minimal_cfg):
+        """Synthetic dataset passed as a raw dict should not raise."""
+        cfg = self._make_cfg(
+            minimal_cfg,
+            [
+                {
+                    "path": "synthetic",
+                    "type": "_synthetic",
+                    "length": 100,
+                    "sequence_length": 64,
+                }
+            ],
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg.datasets[0]["path"] == "synthetic"
+
+    def test_synthetic_already_sft_does_not_crash(self, minimal_cfg):
+        """Synthetic dataset already parsed as SFTDataset should not raise AttributeError."""
+        sft = SFTDataset(path="synthetic", type="_synthetic")
+        cfg = self._make_cfg(minimal_cfg, [sft])
+
+        # Before the fix, this raised:
+        #   AttributeError: 'SFTDataset' object has no attribute 'get'
+        new_cfg = validate_config(cfg)
+        assert new_cfg.datasets[0]["path"] == "synthetic"
+
+    def test_non_synthetic_sft_validates(self, minimal_cfg):
+        """A regular SFT dataset should validate without being treated as synthetic."""
+        cfg = self._make_cfg(
+            minimal_cfg,
+            [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg.datasets[0]["path"] == "mhenrichsen/alpaca_2k_test"

tests/prompt_strategies/test_synthetic.py

@@ -0,0 +1,125 @@
+"""Tests for the synthetic dataset generator."""
+
+import unittest
+from unittest.mock import MagicMock
+
+from datasets import Dataset
+
+from axolotl.prompt_strategies._synthetic import SyntheticDatasetStrategy, load
+from axolotl.utils.dict import DictDefault
+
+
+class TestSyntheticDatasetStrategy(unittest.TestCase):
+    def test_generates_correct_shape(self):
+        strategy = SyntheticDatasetStrategy(
+            sequence_length=128,
+            length=50,
+            min_input_id=1,
+            max_input_id=1000,
+            seed=42,
+        )
+        dummy = Dataset.from_dict({"text": [""]})
+        result = strategy.wrap_dataset(dummy)
+
+        assert len(result) == 50
+        assert len(result[0]["input_ids"]) == 128
+        assert len(result[0]["attention_mask"]) == 128
+        assert len(result[0]["labels"]) == 128
+
+    def test_attention_mask_all_ones(self):
+        strategy = SyntheticDatasetStrategy(sequence_length=64, length=10, seed=0)
+        dummy = Dataset.from_dict({"text": [""]})
+        result = strategy.wrap_dataset(dummy)
+
+        for row in result:
+            assert all(v == 1 for v in row["attention_mask"])
+
+    def test_labels_equal_input_ids(self):
+        strategy = SyntheticDatasetStrategy(sequence_length=64, length=10, seed=0)
+        dummy = Dataset.from_dict({"text": [""]})
+        result = strategy.wrap_dataset(dummy)
+
+        for row in result:
+            assert row["input_ids"] == row["labels"]
+
+    def test_input_id_range(self):
+        strategy = SyntheticDatasetStrategy(
+            sequence_length=64,
+            length=100,
+            min_input_id=500,
+            max_input_id=600,
+            seed=42,
+        )
+        dummy = Dataset.from_dict({"text": [""]})
+        result = strategy.wrap_dataset(dummy)
+
+        for row in result:
+            for token_id in row["input_ids"]:
+                assert 500 <= token_id < 600
+
+    def test_seed_reproducibility(self):
+        kwargs = dict(
+            sequence_length=64, length=20, min_input_id=1, max_input_id=1000, seed=123
+        )
+        dummy = Dataset.from_dict({"text": [""]})
+
+        result1 = SyntheticDatasetStrategy(**kwargs).wrap_dataset(dummy)
+        result2 = SyntheticDatasetStrategy(**kwargs).wrap_dataset(dummy)
+
+        for r1, r2 in zip(result1, result2, strict=True):
+            assert r1["input_ids"] == r2["input_ids"]
+
+    def test_different_seeds_differ(self):
+        common = dict(sequence_length=64, length=20, min_input_id=1, max_input_id=1000)
+        dummy = Dataset.from_dict({"text": [""]})
+
+        result1 = SyntheticDatasetStrategy(seed=1, **common).wrap_dataset(dummy)
+        result2 = SyntheticDatasetStrategy(seed=2, **common).wrap_dataset(dummy)
+
+        any_different = any(
+            r1["input_ids"] != r2["input_ids"]
+            for r1, r2 in zip(result1, result2, strict=True)
+        )
+        assert any_different
+
+    def test_load_function_with_ds_cfg(self):
+        tokenizer = MagicMock()
+        tokenizer.vocab_size = 32000
+        cfg = DictDefault({"sequence_len": 512, "train_on_inputs": False})
+        ds_cfg = {
+            "sequence_length": 256,
+            "length": 5,
+            "min_input_id": 10,
+            "max_input_id": 100,
+            "seed": 0,
+        }
+
+        strategy = load(tokenizer, cfg, ds_cfg=ds_cfg)
+        assert isinstance(strategy, SyntheticDatasetStrategy)
+        assert strategy.sequence_length == 256
+        assert strategy.length == 5
+        assert strategy.min_input_id == 10
+        assert strategy.max_input_id == 100
+
+    def test_load_defaults_from_cfg(self):
+        tokenizer = MagicMock()
+        tokenizer.vocab_size = 32000
+        cfg = DictDefault({"sequence_len": 1024, "train_on_inputs": False})
+
+        strategy = load(tokenizer, cfg, ds_cfg={})
+        assert strategy.sequence_length == 1024
+        assert strategy.max_input_id == 32000
+        assert strategy.length == 1000
+
+    def test_load_with_no_ds_cfg(self):
+        tokenizer = MagicMock()
+        tokenizer.vocab_size = 50000
+        cfg = DictDefault({"sequence_len": 2048, "train_on_inputs": False})
+
+        strategy = load(tokenizer, cfg)
+        assert strategy.sequence_length == 2048
+        assert strategy.max_input_id == 50000
+
+
+if __name__ == "__main__":
+    unittest.main()