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

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()