Add custom fingerprint support to from_generator

src/datasets/arrow_dataset.py

@@ -1120,6 +1120,7 @@ def from_generator(
         gen_kwargs: Optional[dict] = None,
         num_proc: Optional[int] = None,
         split: NamedSplit = Split.TRAIN,
+        fingerprint: Optional[str] = None,
         **kwargs,
     ):
         """Create a Dataset from a generator.
@@ -1146,6 +1147,12 @@ def from_generator(
                 Split name to be assigned to the dataset.
 
                 <Added version="2.21.0"/>
+            fingerprint (`str`, *optional*):
+                Fingerprint that will be used to generate dataset ID.
+                By default `fingerprint` is generated by hashing the generator function and all the args which can be slow
+                if it uses large objects like AI models.
+
+                <Added version="4.3.0"/>
             **kwargs (additional keyword arguments):
                 Keyword arguments to be passed to :[`GeneratorConfig`].
 
@@ -1183,6 +1190,7 @@ def from_generator(
             gen_kwargs=gen_kwargs,
             num_proc=num_proc,
             split=split,
+            fingerprint=fingerprint,
             **kwargs,
         ).read()
 

src/datasets/builder.py

@@ -313,6 +313,7 @@ def __init__(
         data_dir: Optional[str] = None,
         storage_options: Optional[dict] = None,
         writer_batch_size: Optional[int] = None,
+        config_id: Optional[str] = None,
         **config_kwargs,
     ):
         # DatasetBuilder name
@@ -343,6 +344,7 @@ def __init__(
         self.config, self.config_id = self._create_builder_config(
             config_name=config_name,
             custom_features=features,
+            config_id=config_id,
             **config_kwargs,
         )
 
@@ -502,7 +504,7 @@ def update_hash_with_config_parameters(hash: str, config_parameters: dict) -> st
                 return legacy_relative_data_dir
 
     def _create_builder_config(
-        self, config_name=None, custom_features=None, **config_kwargs
+        self, config_name=None, custom_features=None, config_id=None, **config_kwargs
     ) -> tuple[BuilderConfig, str]:
         """Create and validate BuilderConfig object as well as a unique config id for this config.
         Raises ValueError if there are multiple builder configs and config_name and DEFAULT_CONFIG_NAME are None.
@@ -570,10 +572,11 @@ def _create_builder_config(
         )
 
         # compute the config id that is going to be used for caching
-        config_id = builder_config.create_config_id(
-            config_kwargs,
-            custom_features=custom_features,
-        )
+        if config_id is None:
+            config_id = builder_config.create_config_id(
+                config_kwargs,
+                custom_features=custom_features,
+            )
         is_custom = (config_id not in self.builder_configs) and config_id != "default"
         if is_custom:
             logger.info(f"Using custom data configuration {config_id}")

src/datasets/io/generator.py

@@ -16,6 +16,7 @@ def __init__(
         gen_kwargs: Optional[dict] = None,
         num_proc: Optional[int] = None,
         split: NamedSplit = Split.TRAIN,
+        fingerprint: Optional[str] = None,
         **kwargs,
     ):
         super().__init__(
@@ -32,6 +33,7 @@ def __init__(
             generator=generator,
             gen_kwargs=gen_kwargs,
             split=split,
+            config_id="default-fingerprint=" + fingerprint if fingerprint else None,
             **kwargs,
         )
 
@@ -56,4 +58,6 @@ def read(self):
             dataset = self.builder.as_dataset(
                 split=self.builder.config.split, verification_mode=verification_mode, in_memory=self.keep_in_memory
             )
+            if self.fingerprint:
+                dataset._fingerprint = self.fingerprint
         return dataset

tests/test_arrow_dataset.py

@@ -4114,6 +4114,16 @@ def test_dataset_from_generator_split(split, data_generator, tmp_path):
     _check_generator_dataset(dataset, expected_features, expected_split)
 
 
+@pytest.mark.parametrize("fingerprint", [None, "test-dataset"])
+def test_dataset_from_generator_fingerprint(fingerprint, data_generator, tmp_path):
+    cache_dir = tmp_path / "cache"
+    expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
+    dataset = Dataset.from_generator(data_generator, cache_dir=cache_dir, fingerprint=fingerprint)
+    _check_generator_dataset(dataset, expected_features, NamedSplit("train"))
+    if fingerprint:
+        assert dataset._fingerprint == fingerprint
+
+
 @require_not_windows
 @require_dill_gt_0_3_2
 @require_pyspark