fix

paddlenlp/transformers/llama/tokenizer.py

@@ -72,9 +72,7 @@ def __init__(
         self.add_bos_token = add_bos_token
         self.add_eos_token = add_eos_token
         self.decode_with_prefix_space = decode_with_prefix_space
-        # self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
-        self.sp_model.Load(vocab_file)
+        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", True))
 
     @property
     def vocab_size(self):
@@ -101,7 +99,7 @@ def bos_token_id(self) -> Optional[int]:
     def eos_token_id(self) -> Optional[int]:
         return self.sp_model.eos_id()
 
-    def get_spm_processor(self, from_slow=False):
+    def get_spm_processor(self, from_slow=True):
         tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         if from_slow:  # no dependency on protobuf
             tokenizer.Load(self.vocab_file)

paddlenlp/transformers/tokenizer_utils_base.py

@@ -1600,7 +1600,10 @@ def _from_pretrained(
         from_hf_hub=False,
         **kwargs,
     ):
-        from_slow = kwargs.get("from_slow", False)
+        if cls.__name__.endswith("Fast"):
+            from_slow = kwargs.get("from_slow", False)
+        else:
+            from_slow = kwargs.get("from_slow", True)
         has_tokenizer_file = resolved_vocab_files.get("tokenizer_file", None) is not None
         if (from_slow or not has_tokenizer_file) and cls.slow_tokenizer_class is not None:
             slow_tokenizer = (cls.slow_tokenizer_class)._from_pretrained(

tests/transformers/mbart50/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.

tests/transformers/mbart50/test_tokenizer.py

@@ -0,0 +1,191 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 tempfile
+import unittest
+
+from paddlenlp.transformers import SPIECE_UNDERLINE, MBart50Tokenizer
+from paddlenlp.transformers.mbart.modeling import shift_tokens_right
+
+from ...testing_utils import get_tests_dir, nested_simplify
+from ..test_tokenizer_common import TokenizerTesterMixin
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+EN_CODE = 250004
+RO_CODE = 250020
+
+
+class MBart50TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+    tokenizer_class = MBart50Tokenizer
+    test_sentencepiece = True
+
+    test_offsets = False
+
+    def setUp(self):
+        super().setUp()
+
+        # We have a SentencePiece fixture for testing
+        tokenizer = MBart50Tokenizer(SAMPLE_VOCAB, src_lang="en_XX", tgt_lang="ro_RO", keep_accents=True)
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    def test_convert_token_and_id(self):
+        """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
+        token = "<s>"
+        token_id = 0
+
+        self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
+        self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
+
+    def test_get_vocab(self):
+        vocab_keys = list(self.get_tokenizer().get_vocab().keys())
+
+        self.assertEqual(vocab_keys[0], "<s>")
+        self.assertEqual(vocab_keys[1], "<pad>")
+        self.assertEqual(vocab_keys[-1], "<mask>")
+        self.assertEqual(len(vocab_keys), 1_054)
+
+    def test_vocab_size(self):
+        self.assertEqual(self.get_tokenizer().vocab_size, 1_054)
+
+    def test_full_tokenizer(self):
+        tokenizer = MBart50Tokenizer(SAMPLE_VOCAB, src_lang="en_XX", tgt_lang="ro_RO", keep_accents=True)
+
+        tokens = tokenizer.tokenize("This is a test")
+        self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
+
+        self.assertListEqual(
+            tokenizer.convert_tokens_to_ids(tokens),
+            [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
+        )
+
+        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
+        self.assertListEqual(
+            tokens,
+            # fmt: off
+            [
+                SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was",
+                SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in",
+                SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",",
+                SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this",
+                SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é",
+                "."
+            ],
+            # fmt: on
+        )
+        ids = tokenizer.convert_tokens_to_ids(tokens)
+        self.assertListEqual(
+            ids,
+            [
+                value + tokenizer.fairseq_offset
+                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
+            ],
+        )
+
+        back_tokens = tokenizer.convert_ids_to_tokens(ids)
+        self.assertListEqual(
+            back_tokens,
+            # fmt: off
+            [
+                SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was",
+                SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in",
+                SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",",
+                SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this",
+                SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s",
+                "<unk>", "."
+            ],
+            # fmt: on
+        )
+
+
+class MBart50OneToManyIntegrationTest(unittest.TestCase):
+    checkpoint_name = "mbart-large-50-one-to-many-mmt"
+    src_text = [
+        " UN Chief Says There Is No Military Solution in Syria",
+        """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
+    ]
+    tgt_text = [
+        "Şeful ONU declară că nu există o soluţie militară în Siria",
+        "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
+        ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
+        " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
+    ]
+    expected_src_tokens = [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tokenizer: MBart50Tokenizer = MBart50Tokenizer.from_pretrained(
+            cls.checkpoint_name, src_lang="en_XX", tgt_lang="ro_RO"
+        )
+        cls.pad_token_id = 1
+        return cls
+
+    def check_language_codes(self):
+        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"], 250001)
+        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"], 250004)
+        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"], 250020)
+        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"], 250038)
+
+    def test_tokenizer_decode_ignores_language_codes(self):
+        self.assertIn(RO_CODE, self.tokenizer.all_special_ids)
+        generated_ids = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
+        result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
+        expected_romanian = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
+        self.assertEqual(result, expected_romanian)
+        self.assertNotIn(self.tokenizer.eos_token, result)
+
+    def test_tokenizer_truncation(self):
+        src_text = ["this is gunna be a long sentence " * 20]
+        assert isinstance(src_text[0], str)
+        desired_max_length = 10
+        ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
+        self.assertEqual(ids[0], EN_CODE)
+        self.assertEqual(ids[-1], 2)
+        self.assertEqual(len(ids), desired_max_length)
+
+    def test_mask_token(self):
+        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]), [250053, 250001])
+
+    def test_special_tokens_unaffacted_by_save_load(self):
+        tmpdirname = tempfile.mkdtemp()
+        original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
+        self.tokenizer.save_pretrained(tmpdirname)
+        new_tok = MBart50Tokenizer.from_pretrained(tmpdirname)
+        self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
+
+    def test_seq2seq_max_target_length(self):
+        batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pd")
+        targets = self.tokenizer(self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pd")
+        labels = targets["input_ids"]
+        batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)
+
+        self.assertEqual(batch.input_ids.shape[1], 3)
+        self.assertEqual(batch.decoder_input_ids.shape[1], 10)
+
+    def test_tokenizer_translation(self):
+        inputs = self.tokenizer._build_translation_inputs(
+            "A test", return_tensors="pd", src_lang="en_XX", tgt_lang="ar_AR"
+        )
+
+        self.assertEqual(
+            nested_simplify(inputs),
+            {
+                # en_XX, A, test, EOS
+                "input_ids": [[250004, 62, 3034, 2]],
+                "attention_mask": [[1, 1, 1, 1]],
+                # ar_AR
+                "forced_bos_token_id": 250001,
+            },
+        )