Support skip_trying_type

src/datasets/arrow_dataset.py

@@ -2851,6 +2851,7 @@ def map(
         suffix_template: str = "_{rank:05d}_of_{num_proc:05d}",
         new_fingerprint: Optional[str] = None,
         desc: Optional[str] = None,
+        try_original_type: Optional[bool] = True,
     ) -> "Dataset":
         """
         Apply a function to all the examples in the table (individually or in batches) and update the table.
@@ -2932,6 +2933,9 @@ def map(
                 If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments.
             desc (`str`, *optional*, defaults to `None`):
                 Meaningful description to be displayed alongside with the progress bar while mapping examples.
+            try_original_type (`Optional[bool]`, defaults to `True`):
+                Try to keep the types of the original columns (e.g. int32 -> int32).
+                Set to False if you want to always infer new types.
 
         Example:
 
@@ -3022,6 +3026,7 @@ def map(
             "features": features,
             "disable_nullable": disable_nullable,
             "fn_kwargs": fn_kwargs,
+            "try_original_type": try_original_type,
         }
 
         if new_fingerprint is None:
@@ -3216,6 +3221,7 @@ def _map_single(
         new_fingerprint: Optional[str] = None,
         rank: Optional[int] = None,
         offset: int = 0,
+        try_original_type: Optional[bool] = True,
     ) -> Iterable[tuple[int, bool, Union[int, "Dataset"]]]:
         """Apply a function to all the elements in the table (individually or in batches)
         and update the table (if function does update examples).
@@ -3257,6 +3263,9 @@ def _map_single(
                 If `None`, the new fingerprint is computed using a hash of the previous fingerprint, and the transform arguments
             rank: (`int`, optional, defaults to `None`): If specified, this is the process rank when doing multiprocessing
             offset: (`int`, defaults to 0): If specified, this is an offset applied to the indices passed to `function` if `with_indices=True`.
+            try_original_type: (`Optional[bool]`, defaults to `True`):
+                Try to keep the types of the original columns (e.g. int32 -> int32).
+                Set to False if you want to always infer new types.
         """
         if fn_kwargs is None:
             fn_kwargs = {}
@@ -3528,7 +3537,7 @@ def iter_outputs(shard_iterable):
                             ):
                                 writer.write_table(batch.to_arrow())
                             else:
-                                writer.write_batch(batch)
+                                writer.write_batch(batch, try_original_type=try_original_type)
                         num_examples_progress_update += num_examples_in_batch
                         if time.time() > _time + config.PBAR_REFRESH_TIME_INTERVAL:
                             _time = time.time()

src/datasets/arrow_writer.py

@@ -584,13 +584,15 @@ def write_batch(
         self,
         batch_examples: dict[str, list],
         writer_batch_size: Optional[int] = None,
+        try_original_type: Optional[bool] = True,
     ):
         """Write a batch of Example to file.
         Ignores the batch if it appears to be empty,
         preventing a potential schema update of unknown types.
 
         Args:
             batch_examples: the batch of examples to add.
+            try_original_type: use `try_type` when instantiating OptimizedTypedSequence if `True`, otherwise `try_type = None`.
         """
         if batch_examples and len(next(iter(batch_examples.values()))) == 0:
             return
@@ -615,7 +617,11 @@ def write_batch(
                 arrays.append(array)
                 inferred_features[col] = generate_from_arrow_type(col_values.type)
             else:
-                col_try_type = try_features[col] if try_features is not None and col in try_features else None
+                col_try_type = (
+                    try_features[col]
+                    if try_features is not None and col in try_features and try_original_type
+                    else None
+                )
                 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)
                 arrays.append(pa.array(typed_sequence))
                 inferred_features[col] = typed_sequence.get_inferred_type()

tests/test_arrow_dataset.py

@@ -127,7 +127,13 @@ def inject_fixtures(self, caplog, set_sqlalchemy_silence_uber_warning):
         self._caplog = caplog
 
     def _create_dummy_dataset(
-        self, in_memory: bool, tmp_dir: str, multiple_columns=False, array_features=False, nested_features=False
+        self,
+        in_memory: bool,
+        tmp_dir: str,
+        multiple_columns=False,
+        array_features=False,
+        nested_features=False,
+        int_to_float=False,
     ) -> Dataset:
         assert int(multiple_columns) + int(array_features) + int(nested_features) < 2
         if multiple_columns:
@@ -151,6 +157,12 @@ def _create_dummy_dataset(
             data = {"nested": [{"a": i, "x": i * 10, "c": i * 100} for i in range(1, 11)]}
             features = Features({"nested": {"a": Value("int64"), "x": Value("int64"), "c": Value("int64")}})
             dset = Dataset.from_dict(data, features=features)
+        elif int_to_float:
+            data = {
+                "text": ["text1", "text2", "text3", "text4"],
+                "labels": [[1, 1, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 1, 1], [0, 0, 0, 1, 0]],
+            }
+            dset = Dataset.from_dict(data)
         else:
             dset = Dataset.from_dict({"filename": ["my_name-train" + "_" + str(x) for x in np.arange(30).tolist()]})
         if not in_memory:
@@ -1123,6 +1135,27 @@ def func(x, i):
                     self.assertListEqual(sorted(dset_test[0].keys()), ["col_1", "col_1_plus_one"])
                     self.assertListEqual(sorted(dset_test.column_names), ["col_1", "col_1_plus_one", "col_2", "col_3"])
                     assert_arrow_metadata_are_synced_with_dataset_features(dset_test)
+        # casting int labels to float labels
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            with self._create_dummy_dataset(in_memory, tmp_dir, int_to_float=True) as dset:
+
+                def _preprocess(examples):
+                    result = {"labels": [list(map(float, labels)) for labels in examples["labels"]]}
+                    return result
+
+                with dset.map(
+                    _preprocess, remove_columns=["labels", "text"], batched=True, try_original_type=True
+                ) as dset_test:
+                    for labels in dset_test["labels"]:
+                        for label in labels:
+                            self.assertIsInstance(label, int)
+
+                with dset.map(
+                    _preprocess, remove_columns=["labels", "text"], batched=True, try_original_type=False
+                ) as dset_test:
+                    for labels in dset_test["labels"]:
+                        for label in labels:
+                            self.assertIsInstance(label, float)
 
     def test_map_multiprocessing(self, in_memory):
         with tempfile.TemporaryDirectory() as tmp_dir:  # standard