Move test model folders (#17034)
* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
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tests/models/t5/__init__.py
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tests/models/t5/__init__.py
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tests/models/t5/test_modeling_flax_t5.py
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tests/models/t5/test_modeling_flax_t5.py
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tests/models/t5/test_modeling_t5.py
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tests/models/t5/test_modeling_t5.py
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tests/models/t5/test_modeling_tf_t5.py
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tests/models/t5/test_modeling_tf_t5.py
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tests/models/t5/test_tokenization_t5.py
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tests/models/t5/test_tokenization_t5.py
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# coding=utf-8
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# Copyright 2018 Google T5 Authors and 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 json
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import os
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import tempfile
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import unittest
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from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, T5Tokenizer, T5TokenizerFast
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from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
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from transformers.utils import cached_property, is_tf_available, is_torch_available
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from ...test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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if is_torch_available():
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FRAMEWORK = "pt"
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elif is_tf_available():
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FRAMEWORK = "tf"
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else:
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FRAMEWORK = "jax"
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@require_sentencepiece
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@require_tokenizers
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class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = T5Tokenizer
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rust_tokenizer_class = T5TokenizerFast
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test_rust_tokenizer = True
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test_sentencepiece = True
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = T5Tokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "<s>"
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token_id = 1
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[0], "<unk>")
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self.assertEqual(vocab_keys[1], "<s>")
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self.assertEqual(vocab_keys[-1], "<pad>")
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self.assertEqual(len(vocab_keys), 1_101)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 1_100)
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def test_full_tokenizer(self):
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tokenizer = T5Tokenizer(SAMPLE_VOCAB)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382])
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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".",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4])
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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".",
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],
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)
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@cached_property
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def t5_base_tokenizer(self):
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return T5Tokenizer.from_pretrained("t5-base")
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@cached_property
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def t5_base_tokenizer_fast(self):
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return T5TokenizerFast.from_pretrained("t5-base")
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def get_tokenizer(self, **kwargs) -> T5Tokenizer:
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return self.tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs)
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def get_rust_tokenizer(self, **kwargs) -> T5TokenizerFast:
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return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs)
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def test_rust_and_python_full_tokenizers(self):
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer()
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sequence = "I was born in 92000, and this is falsé."
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tokens = tokenizer.tokenize(sequence)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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rust_tokenizer = self.get_rust_tokenizer()
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ids = tokenizer.encode(sequence)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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def test_eos_treatment(self):
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tokenizer = self.t5_base_tokenizer
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batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
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batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
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self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])
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def test_prepare_batch(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, tokenizer.eos_token_id]
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batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
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self.assertIsInstance(batch, BatchEncoding)
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if FRAMEWORK != "jax":
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result = list(batch.input_ids.numpy()[0])
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else:
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result = list(batch.input_ids.tolist()[0])
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self.assertListEqual(expected_src_tokens, result)
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self.assertEqual((2, 9), batch.input_ids.shape)
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self.assertEqual((2, 9), batch.attention_mask.shape)
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def test_empty_target_text(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
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# check if input_ids are returned and no decoder_input_ids
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self.assertIn("input_ids", batch)
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self.assertIn("attention_mask", batch)
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self.assertNotIn("decoder_input_ids", batch)
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self.assertNotIn("decoder_attention_mask", batch)
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def test_max_length(self):
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tokenizer = self.t5_base_tokenizer
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tgt_text = [
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"Summary of the text.",
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"Another summary.",
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]
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with tokenizer.as_target_tokenizer():
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targets = tokenizer(
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tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
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)
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self.assertEqual(32, targets["input_ids"].shape[1])
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def test_outputs_not_longer_than_maxlen(self):
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tokenizer = self.t5_base_tokenizer
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batch = tokenizer(
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["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK
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)
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self.assertIsInstance(batch, BatchEncoding)
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# Since T5 does NOT have a max input length,
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# this test should be changed to the following in Transformers v5:
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# self.assertEqual(batch.input_ids.shape, (2, 8001))
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self.assertEqual(batch.input_ids.shape, (2, 512))
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def test_eos_in_input(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization. </s>"]
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tgt_text = ["Summary of the text. </s>"]
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expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, 1]
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expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1]
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batch = tokenizer(src_text)
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with tokenizer.as_target_tokenizer():
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targets = tokenizer(tgt_text)
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self.assertEqual(expected_src_tokens, batch["input_ids"][0])
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self.assertEqual(expected_tgt_tokens, targets["input_ids"][0])
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def test_token_type_ids(self):
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src_text_1 = ["A first paragraph for summarization."]
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src_text_2 = ["A second paragraph for summarization."]
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fast_token_type_ids = self.t5_base_tokenizer_fast(
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src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
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).token_type_ids
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slow_token_type_ids = self.t5_base_tokenizer(
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src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
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).token_type_ids
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self.assertEqual(slow_token_type_ids, fast_token_type_ids)
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self.assertEqual(len(slow_token_type_ids[0]), 18)
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def test_fast_and_slow_same_result(self):
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src_text = "<pad> Today is <unk> nice day </s>"
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tgt_ids = [0, 1960, 19, 2, 1245, 239, 1]
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tgt_text = "<pad> Today is<unk> nice day</s>"
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fast_ids = self.t5_base_tokenizer_fast(src_text, add_special_tokens=False).input_ids
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slow_ids = self.t5_base_tokenizer(src_text, add_special_tokens=False).input_ids
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self.assertEqual(tgt_ids, fast_ids)
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self.assertEqual(tgt_ids, slow_ids)
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fast_text = self.t5_base_tokenizer_fast.decode(fast_ids)
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slow_text = self.t5_base_tokenizer.decode(fast_ids)
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self.assertEqual(tgt_text, fast_text)
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self.assertEqual(tgt_text, slow_text)
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def test_special_tokens_initialization(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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added_tokens = [f"<extra_id_{i}>" for i in range(100)] + [AddedToken("<special>", lstrip=True)]
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
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)
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tokenizer_p = self.tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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p_output = tokenizer_p.encode("Hey this is a <special> token")
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r_output = tokenizer_r.encode("Hey this is a <special> token")
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cr_output = tokenizer_cr.encode("Hey this is a <special> token")
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special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
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self.assertEqual(p_output, r_output)
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self.assertEqual(cr_output, r_output)
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self.assertTrue(special_token_id in p_output)
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self.assertTrue(special_token_id in r_output)
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self.assertTrue(special_token_id in cr_output)
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def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
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tokenizer_list = []
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if self.test_slow_tokenizer:
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tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
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if self.test_rust_tokenizer:
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tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
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for tokenizer_class, tokenizer_utils in tokenizer_list:
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with tempfile.TemporaryDirectory() as tmp_dir:
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tokenizer_utils.save_pretrained(tmp_dir)
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with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
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special_tokens_map = json.load(json_file)
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with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
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tokenizer_config = json.load(json_file)
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added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)]
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special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
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"an_additional_special_token"
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]
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tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
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"an_additional_special_token"
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]
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with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
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json.dump(special_tokens_map, outfile)
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with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
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json.dump(tokenizer_config, outfile)
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# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
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# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
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# "special_tokens_map.json" files
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tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
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tmp_dir,
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)
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self.assertIn(
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"an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
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)
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# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
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self.assertEqual(
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["an_additional_special_token"],
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tokenizer_without_change_in_init.convert_ids_to_tokens(
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tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
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),
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)
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# Now we test that we can change the value of additional_special_tokens in the from_pretrained
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new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
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tokenizer = tokenizer_class.from_pretrained(
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tmp_dir,
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additional_special_tokens=new_added_tokens,
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)
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self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
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self.assertEqual(
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["a_new_additional_special_token"],
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tokenizer.convert_ids_to_tokens(
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||||
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
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||||
),
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||||
)
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||||
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||||
# overwritten from `test_tokenization_common` since T5 has no max length
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||||
def test_pretrained_model_lists(self):
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||||
# We should have at least one default checkpoint for each tokenizer
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# We should specify the max input length as well (used in some part to list the pretrained checkpoints)
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||||
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1)
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||||
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1)
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||||
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||||
@slow
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||||
def test_tokenizer_integration(self):
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||||
# fmt: off
|
||||
expected_encoding = {'input_ids': [[31220, 7, 41, 14034, 801, 38, 3, 102, 63, 17, 127, 524, 18, 7031, 2032, 277, 11, 3, 102, 63, 17, 127, 524, 18, 2026, 17, 10761, 18, 7041, 61, 795, 879, 18, 19681, 4648, 7, 41, 12920, 382, 6, 350, 6383, 4949, 6, 2158, 12920, 382, 9, 6, 3, 4, 11160, 6, 2043, 17153, 279, 49, 17, 6, 3, 4, 434, 9688, 11439, 21, 6869, 10509, 17725, 41, 567, 9138, 61, 11, 6869, 10509, 11946, 41, 18207, 517, 61, 28, 147, 3538, 1220, 7140, 10761, 2250, 16, 910, 1220, 8024, 11, 1659, 1413, 32, 883, 2020, 344, 2215, 226, 6, 12901, 382, 127, 524, 11, 4738, 7, 127, 15390, 5, 1], [272, 24203, 19, 876, 12, 554, 18, 9719, 1659, 2647, 26352, 6497, 7, 45, 73, 9339, 400, 26, 1499, 57, 22801, 10760, 30, 321, 646, 11, 269, 2625, 16, 66, 7500, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [37, 1704, 4216, 3, 20400, 4418, 7, 147, 8, 19743, 1782, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
|
||||
# fmt: on
|
||||
|
||||
self.tokenizer_integration_test_util(
|
||||
expected_encoding=expected_encoding,
|
||||
model_name="t5-base",
|
||||
revision="5a7ff2d8f5117c194c7e32ec1ccbf04642cca99b",
|
||||
)
|
||||
Reference in New Issue
Block a user