Ability to pickle/unpickle BatchEncoding pickle (reimport) (#5039)

* Added is_fast property on BatchEncoding to indicate if the object comes from a Fast Tokenizer.

* Added __get_state__() & __set_state__() to be pickable.

* Correct tokens() return type from List[int] to List[str]

* Added unittest for BatchEncoding pickle/unpickle

* Added unittest for BatchEncoding is_fast

* More careful checking on BatchEncoding unpickle tests.

* Formatting.

* is_fast should assertTrue on Rust tokenizers.

* Ensure tensorflow has correct way of checking array_equal

* More formatting.

src/transformers/tokenization_utils_base.py

@@ -155,6 +155,14 @@ class BatchEncoding(UserDict):
 
         self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
 
+    @property
+    def is_fast(self):
+        """
+        Indicate if this BatchEncoding was generated from the result of a PreTrainedTokenizerFast
+        Returns: True if generated from subclasses of PreTrainedTokenizerFast, else otherwise
+        """
+        return self._encodings is not None
+
     def __getitem__(self, item: Union[int, str]) -> EncodingFast:
         """ If the key is a string, get the value of the dict associated to `key` ('input_ids', 'attention_mask'...)
             If the key is an integer, get the EncodingFast for batch item with index `key`
@@ -175,6 +183,16 @@ class BatchEncoding(UserDict):
         except KeyError:
             raise AttributeError
 
+    def __getstate__(self):
+        return {"data": self.data, "encodings": self._encodings}
+
+    def __setstate__(self, state):
+        if "data" in state:
+            self.data = state["data"]
+
+        if "encodings" in state:
+            self._encodings = state["encodings"]
+
     def keys(self):
         return self.data.keys()
 
@@ -197,7 +215,7 @@ class BatchEncoding(UserDict):
         """
         return self._encodings
 
-    def tokens(self, batch_index: int = 0) -> List[int]:
+    def tokens(self, batch_index: int = 0) -> List[str]:
         if not self._encodings:
             raise ValueError("tokens() is not available when using Python based tokenizers")
         return self._encodings[batch_index].tokens

tests/test_tokenization_utils.py

@@ -12,14 +12,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-
+import pickle
 import unittest
+from typing import Callable, Optional
 
-from transformers import PreTrainedTokenizer, TensorType
+from transformers import BatchEncoding, BertTokenizer, BertTokenizerFast, PreTrainedTokenizer, TensorType
 from transformers.tokenization_gpt2 import GPT2Tokenizer
 
-from .utils import slow
+from .utils import require_tf, require_torch, slow
 
 
 class TokenizerUtilsTest(unittest.TestCase):
@@ -36,11 +36,103 @@ class TokenizerUtilsTest(unittest.TestCase):
                 special_tok_id = tokenizer.convert_tokens_to_ids(special_tok)
                 self.assertIsInstance(special_tok_id, int)
 
+    def assert_dump_and_restore(self, be_original: BatchEncoding, equal_op: Optional[Callable] = None):
+        batch_encoding_str = pickle.dumps(be_original)
+        self.assertIsNotNone(batch_encoding_str)
+
+        be_restored = pickle.loads(batch_encoding_str)
+
+        # Ensure is_fast is correctly restored
+        self.assertEqual(be_restored.is_fast, be_original.is_fast)
+
+        # Ensure encodings are potentially correctly restored
+        if be_original.is_fast:
+            self.assertIsNotNone(be_restored.encodings)
+        else:
+            self.assertIsNone(be_restored.encodings)
+
+        # Ensure the keys are the same
+        for original_v, restored_v in zip(be_original.values(), be_restored.values()):
+            if equal_op:
+                self.assertTrue(equal_op(restored_v, original_v))
+            else:
+                self.assertEqual(restored_v, original_v)
+
     @slow
     def test_pretrained_tokenizers(self):
         self.check_tokenizer_from_pretrained(GPT2Tokenizer)
 
-    def check_tensor_type_from_str(self):
+    def test_tensor_type_from_str(self):
         self.assertEqual(TensorType("tf"), TensorType.TENSORFLOW)
         self.assertEqual(TensorType("pt"), TensorType.PYTORCH)
         self.assertEqual(TensorType("np"), TensorType.NUMPY)
+
+    def test_batch_encoding_pickle(self):
+        import numpy as np
+
+        tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
+        tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
+
+        # Python no tensor
+        with self.subTest("BatchEncoding (Python, return_tensors=None)"):
+            self.assert_dump_and_restore(tokenizer_p("Small example to encode"))
+
+        with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"):
+            self.assert_dump_and_restore(
+                tokenizer_p("Small example to encode", return_tensors=TensorType.NUMPY), np.array_equal
+            )
+
+        with self.subTest("BatchEncoding (Rust, return_tensors=None)"):
+            self.assert_dump_and_restore(tokenizer_r("Small example to encode"))
+
+        with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"):
+            self.assert_dump_and_restore(
+                tokenizer_r("Small example to encode", return_tensors=TensorType.NUMPY), np.array_equal
+            )
+
+    @require_tf
+    def test_batch_encoding_pickle_tf(self):
+        import tensorflow as tf
+
+        def tf_array_equals(t1, t2):
+            return tf.reduce_all(tf.equal(t1, t2))
+
+        tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
+        tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
+
+        with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"):
+            self.assert_dump_and_restore(
+                tokenizer_p("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
+            )
+
+        with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"):
+            self.assert_dump_and_restore(
+                tokenizer_r("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
+            )
+
+    @require_torch
+    def test_batch_encoding_pickle_pt(self):
+        import torch
+
+        tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
+        tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
+
+        with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"):
+            self.assert_dump_and_restore(
+                tokenizer_p("Small example to encode", return_tensors=TensorType.PYTORCH), torch.equal
+            )
+
+        with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"):
+            self.assert_dump_and_restore(
+                tokenizer_r("Small example to encode", return_tensors=TensorType.PYTORCH), torch.equal
+            )
+
+    def test_batch_encoding_is_fast(self):
+        tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
+        tokenizer_r = BertTokenizerFast.from_pretrained("bert-base-cased")
+
+        with self.subTest("Python Tokenizer"):
+            self.assertFalse(tokenizer_p("Small example to_encode").is_fast)
+
+        with self.subTest("Rust Tokenizer"):
+            self.assertTrue(tokenizer_r("Small example to_encode").is_fast)