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Tokenizers should be framework agnostic (#8599)

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* Tokenizers should be framework agnostic

* Run the slow tests

* Not testing

* Fix documentation

* Apply suggestions from code review

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Lysandre Debut
2020-11-17 14:03:03 -05:00
committed by GitHub
parent 7f3b41a306
commit 3095ee9dab
28 changed files with 73 additions and 177 deletions
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20
tests/test_modeling_marian.py
View File

@@ -132,9 +132,9 @@ class MarianIntegrationTest(unittest.TestCase):
self.assertListEqual(self.expected_text, generated_words)
def translate_src_text(self, **tokenizer_kwargs):
model_inputs = self.tokenizer.prepare_seq2seq_batch(src_texts=self.src_text, **tokenizer_kwargs).to(
torch_device
)
model_inputs = self.tokenizer.prepare_seq2seq_batch(
src_texts=self.src_text, return_tensors="pt", **tokenizer_kwargs
).to(torch_device)
self.assertEqual(self.model.device, model_inputs.input_ids.device)
generated_ids = self.model.generate(
model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2, max_length=128
@@ -151,7 +151,9 @@ class TestMarian_EN_DE_More(MarianIntegrationTest):
src, tgt = ["I am a small frog"], ["Ich bin ein kleiner Frosch."]
expected_ids = [38, 121, 14, 697, 38848, 0]
model_inputs: dict = self.tokenizer.prepare_seq2seq_batch(src, tgt_texts=tgt).to(torch_device)
model_inputs: dict = self.tokenizer.prepare_seq2seq_batch(src, tgt_texts=tgt, return_tensors="pt").to(
torch_device
)
self.assertListEqual(expected_ids, model_inputs.input_ids[0].tolist())
@@ -171,12 +173,16 @@ class TestMarian_EN_DE_More(MarianIntegrationTest):
def test_unk_support(self):
t = self.tokenizer
ids = t.prepare_seq2seq_batch(["||"]).to(torch_device).input_ids[0].tolist()
ids = t.prepare_seq2seq_batch(["||"], return_tensors="pt").to(torch_device).input_ids[0].tolist()
expected = [t.unk_token_id, t.unk_token_id, t.eos_token_id]
self.assertEqual(expected, ids)
def test_pad_not_split(self):
input_ids_w_pad = self.tokenizer.prepare_seq2seq_batch(["I am a small frog <pad>"]).input_ids[0].tolist()
input_ids_w_pad = (
self.tokenizer.prepare_seq2seq_batch(["I am a small frog <pad>"], return_tensors="pt")
.input_ids[0]
.tolist()
)
expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0] # pad
self.assertListEqual(expected_w_pad, input_ids_w_pad)
@@ -294,7 +300,7 @@ class TestMarian_en_ROMANCE(MarianIntegrationTest):
normalized = self.tokenizer.normalize("")
self.assertIsInstance(normalized, str)
with self.assertRaises(ValueError):
self.tokenizer.prepare_seq2seq_batch([""])
self.tokenizer.prepare_seq2seq_batch([""], return_tensors="pt")
@slow
def test_pipeline(self):

12
tests/test_modeling_mbart.py
View File

@@ -92,7 +92,7 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
@slow
def test_enro_generate_one(self):
batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(
["UN Chief Says There Is No Military Solution in Syria"]
["UN Chief Says There Is No Military Solution in Syria"], return_tensors="pt"
).to(torch_device)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
@@ -101,7 +101,9 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
@slow
def test_enro_generate_batch(self):
batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(self.src_text).to(torch_device)
batch: BatchEncoding = self.tokenizer.prepare_seq2seq_batch(self.src_text, return_tensors="pt").to(
torch_device
)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
assert self.tgt_text == decoded
@@ -153,7 +155,7 @@ class MBartCC25IntegrationTest(AbstractSeq2SeqIntegrationTest):
@unittest.skip("This test is broken, still generates english")
def test_cc25_generate(self):
inputs = self.tokenizer.prepare_seq2seq_batch([self.src_text[0]]).to(torch_device)
inputs = self.tokenizer.prepare_seq2seq_batch([self.src_text[0]], return_tensors="pt").to(torch_device)
translated_tokens = self.model.generate(
input_ids=inputs["input_ids"].to(torch_device),
decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"],
@@ -163,7 +165,9 @@ class MBartCC25IntegrationTest(AbstractSeq2SeqIntegrationTest):
@slow
def test_fill_mask(self):
inputs = self.tokenizer.prepare_seq2seq_batch(["One of the best <mask> I ever read!"]).to(torch_device)
inputs = self.tokenizer.prepare_seq2seq_batch(["One of the best <mask> I ever read!"], return_tensors="pt").to(
torch_device
)
outputs = self.model.generate(
inputs["input_ids"], decoder_start_token_id=self.tokenizer.lang_code_to_id["en_XX"], num_beams=1
)

2
tests/test_tokenization_common.py
View File

@@ -1794,7 +1794,7 @@ class TokenizerTesterMixin:
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.labels.shape[1], 10)
# max_target_length will default to max_length if not specified
batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, max_length=3)
batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, max_length=3, return_tensors="pt")
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.labels.shape[1], 3)

11
tests/test_tokenization_mbart.py
View File

@@ -165,7 +165,6 @@ class MBartEnroIntegrationTest(unittest.TestCase):
desired_max_length = 10
ids = self.tokenizer.prepare_seq2seq_batch(
src_text,
return_tensors=None,
max_length=desired_max_length,
).input_ids[0]
self.assertEqual(ids[-2], 2)
@@ -203,9 +202,7 @@ class MBartEnroIntegrationTest(unittest.TestCase):
@require_torch
def test_enro_tokenizer_prepare_seq2seq_batch(self):
batch = self.tokenizer.prepare_seq2seq_batch(
self.src_text,
tgt_texts=self.tgt_text,
max_length=len(self.expected_src_tokens),
self.src_text, tgt_texts=self.tgt_text, max_length=len(self.expected_src_tokens), return_tensors="pt"
)
batch["decoder_input_ids"] = shift_tokens_right(batch.labels, self.tokenizer.pad_token_id)
self.assertIsInstance(batch, BatchEncoding)
@@ -221,13 +218,15 @@ class MBartEnroIntegrationTest(unittest.TestCase):
def test_seq2seq_max_target_length(self):
batch = self.tokenizer.prepare_seq2seq_batch(
self.src_text, tgt_texts=self.tgt_text, max_length=3, max_target_length=10
self.src_text, tgt_texts=self.tgt_text, max_length=3, max_target_length=10, return_tensors="pt"
)
batch["decoder_input_ids"] = shift_tokens_right(batch.labels, self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.decoder_input_ids.shape[1], 10)
# max_target_length will default to max_length if not specified
batch = self.tokenizer.prepare_seq2seq_batch(self.src_text, tgt_texts=self.tgt_text, max_length=3)
batch = self.tokenizer.prepare_seq2seq_batch(
self.src_text, tgt_texts=self.tgt_text, max_length=3, return_tensors="pt"
)
batch["decoder_input_ids"] = shift_tokens_right(batch.labels, self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.decoder_input_ids.shape[1], 3)

4
tests/test_tokenization_pegasus.py
View File

@@ -61,7 +61,9 @@ class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
def test_pegasus_large_seq2seq_truncation(self):
src_texts = ["This is going to be way too long." * 150, "short example"]
tgt_texts = ["not super long but more than 5 tokens", "tiny"]
batch = self.pegasus_large_tokenizer.prepare_seq2seq_batch(src_texts, tgt_texts=tgt_texts, max_target_length=5)
batch = self.pegasus_large_tokenizer.prepare_seq2seq_batch(
src_texts, tgt_texts=tgt_texts, max_target_length=5, return_tensors="pt"
)
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert "labels" in batch # because tgt_texts was specified