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279 lines
9.7 KiB
Python
279 lines
9.7 KiB
Python
# coding=utf-8
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# Copyright 2020 HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import is_torch_available
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from transformers.file_utils import cached_property
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from transformers.hf_api import HfApi
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from transformers.testing_utils import require_torch, slow, torch_device
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if is_torch_available():
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import torch
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from transformers import (
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AutoConfig,
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AutoModelWithLMHead,
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AutoTokenizer,
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MarianConfig,
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MarianMTModel,
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MarianTokenizer,
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)
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from transformers.convert_marian_to_pytorch import (
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ORG_NAME,
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convert_hf_name_to_opus_name,
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convert_opus_name_to_hf_name,
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)
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from transformers.modeling_bart import shift_tokens_right
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from transformers.pipelines import TranslationPipeline
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class ModelManagementTests(unittest.TestCase):
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@slow
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def test_model_names(self):
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model_list = HfApi().model_list()
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model_ids = [x.modelId for x in model_list if x.modelId.startswith(ORG_NAME)]
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bad_model_ids = [mid for mid in model_ids if "+" in model_ids]
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self.assertListEqual([], bad_model_ids)
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self.assertGreater(len(model_ids), 500)
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@require_torch
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class MarianIntegrationTest(unittest.TestCase):
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src = "en"
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tgt = "de"
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src_text = [
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"I am a small frog.",
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"Now I can forget the 100 words of german that I know.",
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"Tom asked his teacher for advice.",
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"That's how I would do it.",
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"Tom really admired Mary's courage.",
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"Turn around and close your eyes.",
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]
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expected_text = [
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"Ich bin ein kleiner Frosch.",
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"Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
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"Tom bat seinen Lehrer um Rat.",
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"So würde ich das machen.",
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"Tom bewunderte Marias Mut wirklich.",
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"Drehen Sie sich um und schließen Sie die Augen.",
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]
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# ^^ actual C++ output differs slightly: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen
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@classmethod
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def setUpClass(cls) -> None:
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cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
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cls.tokenizer: MarianTokenizer = AutoTokenizer.from_pretrained(cls.model_name)
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cls.eos_token_id = cls.tokenizer.eos_token_id
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return cls
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@cached_property
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def model(self):
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model: MarianMTModel = AutoModelWithLMHead.from_pretrained(self.model_name).to(torch_device)
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c = model.config
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self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]])
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self.assertEqual(c.max_length, 512)
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self.assertEqual(c.decoder_start_token_id, c.pad_token_id)
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if torch_device == "cuda":
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return model.half()
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else:
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return model
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def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
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generated_words = self.translate_src_text(**tokenizer_kwargs)
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self.assertListEqual(self.expected_text, generated_words)
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def translate_src_text(self, **tokenizer_kwargs):
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model_inputs = self.tokenizer.prepare_seq2seq_batch(src_texts=self.src_text, **tokenizer_kwargs).to(
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torch_device
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)
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self.assertEqual(self.model.device, model_inputs.input_ids.device)
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generated_ids = self.model.generate(
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model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2
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)
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generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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return generated_words
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class TestMarian_EN_DE_More(MarianIntegrationTest):
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@slow
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def test_forward(self):
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src, tgt = ["I am a small frog"], ["Ich bin ein kleiner Frosch."]
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expected_ids = [38, 121, 14, 697, 38848, 0]
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model_inputs: dict = self.tokenizer.prepare_seq2seq_batch(src, tgt_texts=tgt).to(torch_device)
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self.assertListEqual(expected_ids, model_inputs.input_ids[0].tolist())
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desired_keys = {
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"input_ids",
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"attention_mask",
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"labels",
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}
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self.assertSetEqual(desired_keys, set(model_inputs.keys()))
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model_inputs["decoder_input_ids"] = shift_tokens_right(model_inputs.labels, self.tokenizer.pad_token_id)
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model_inputs["return_dict"] = True
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model_inputs["use_cache"] = False
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with torch.no_grad():
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outputs = self.model(**model_inputs)
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max_indices = outputs.logits.argmax(-1)
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self.tokenizer.batch_decode(max_indices)
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def test_unk_support(self):
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t = self.tokenizer
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ids = t.prepare_seq2seq_batch(["||"]).to(torch_device).input_ids[0].tolist()
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expected = [t.unk_token_id, t.unk_token_id, t.eos_token_id]
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self.assertEqual(expected, ids)
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def test_pad_not_split(self):
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input_ids_w_pad = self.tokenizer.prepare_seq2seq_batch(["I am a small frog <pad>"]).input_ids[0].tolist()
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expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0] # pad
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self.assertListEqual(expected_w_pad, input_ids_w_pad)
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@slow
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def test_batch_generation_en_de(self):
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self._assert_generated_batch_equal_expected()
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def test_auto_config(self):
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config = AutoConfig.from_pretrained(self.model_name)
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self.assertIsInstance(config, MarianConfig)
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class TestMarian_EN_FR(MarianIntegrationTest):
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src = "en"
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tgt = "fr"
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src_text = [
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"I am a small frog.",
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"Now I can forget the 100 words of german that I know.",
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]
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expected_text = [
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"Je suis une petite grenouille.",
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"Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
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]
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@slow
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def test_batch_generation_en_fr(self):
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self._assert_generated_batch_equal_expected()
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class TestMarian_FR_EN(MarianIntegrationTest):
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src = "fr"
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tgt = "en"
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src_text = [
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"Donnez moi le micro.",
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"Tom et Mary étaient assis à une table.", # Accents
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]
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expected_text = [
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"Give me the microphone.",
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"Tom and Mary were sitting at a table.",
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]
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@slow
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def test_batch_generation_fr_en(self):
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self._assert_generated_batch_equal_expected()
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class TestMarian_RU_FR(MarianIntegrationTest):
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src = "ru"
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tgt = "fr"
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src_text = ["Он показал мне рукопись своей новой пьесы."]
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expected_text = ["Il m'a montré le manuscrit de sa nouvelle pièce."]
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@slow
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def test_batch_generation_ru_fr(self):
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self._assert_generated_batch_equal_expected()
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class TestMarian_MT_EN(MarianIntegrationTest):
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src = "mt"
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tgt = "en"
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src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
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expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
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@slow
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def test_batch_generation_mt_en(self):
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self._assert_generated_batch_equal_expected()
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class TestMarian_en_zh(MarianIntegrationTest):
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src = "en"
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tgt = "zh"
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src_text = ["My name is Wolfgang and I live in Berlin"]
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expected_text = ["我叫沃尔夫冈 我住在柏林"]
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@slow
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def test_batch_generation_eng_zho(self):
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self._assert_generated_batch_equal_expected()
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class TestMarian_en_ROMANCE(MarianIntegrationTest):
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"""Multilingual on target side."""
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src = "en"
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tgt = "ROMANCE"
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src_text = [
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">>fr<< Don't spend so much time watching TV.",
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">>pt<< Your message has been sent.",
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">>es<< He's two years older than me.",
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]
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expected_text = [
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"Ne passez pas autant de temps à regarder la télé.",
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"A sua mensagem foi enviada.",
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"Es dos años más viejo que yo.",
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]
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@slow
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def test_batch_generation_en_ROMANCE_multi(self):
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self._assert_generated_batch_equal_expected()
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def test_tokenizer_handles_empty(self):
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normalized = self.tokenizer.normalize("")
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self.assertIsInstance(normalized, str)
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with self.assertRaises(ValueError):
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self.tokenizer.prepare_seq2seq_batch([""])
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def test_pipeline(self):
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device = 0 if torch_device == "cuda" else -1
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pipeline = TranslationPipeline(self.model, self.tokenizer, framework="pt", device=device)
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output = pipeline(self.src_text)
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self.assertEqual(self.expected_text, [x["translation_text"] for x in output])
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@require_torch
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class TestConversionUtils(unittest.TestCase):
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def test_renaming_multilingual(self):
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old_names = [
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"opus-mt-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
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"opus-mt-cmn+cn-fi", # no group
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"opus-mt-en-de", # standard name
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"opus-mt-en-de", # standard name
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]
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expected = ["opus-mt-ZH-fi", "opus-mt-cmn_cn-fi", "opus-mt-en-de", "opus-mt-en-de"]
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self.assertListEqual(expected, [convert_opus_name_to_hf_name(x) for x in old_names])
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def test_undoing_renaming(self):
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hf_names = ["opus-mt-ZH-fi", "opus-mt-cmn_cn-fi", "opus-mt-en-de", "opus-mt-en-de"]
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converted_opus_names = [convert_hf_name_to_opus_name(x) for x in hf_names]
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expected_opus_names = [
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"cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
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"cmn+cn-fi",
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"en-de", # standard name
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"en-de",
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]
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self.assertListEqual(expected_opus_names, converted_opus_names)
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