[Test refactor 1/5] Per-folder tests reorganization (#15725)

* Per-folder tests reorganization

Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Stas Bekman <stas@stason.org>

tests/mt5/test_modeling_flax_mt5.py

@@ -0,0 +1,61 @@
+# 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 unittest
+
+from transformers import is_flax_available
+from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
+
+
+if is_flax_available():
+    import optax
+    from flax.training.common_utils import onehot
+    from transformers import AutoTokenizer, FlaxMT5ForConditionalGeneration
+    from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
+
+
+@require_torch
+@require_sentencepiece
+@require_tokenizers
+@require_flax
+class MT5IntegrationTest(unittest.TestCase):
+    @slow
+    def test_small_integration_test(self):
+        """
+        For comparision run:
+        >>> import t5  # pip install t5==0.7.1
+        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
+
+        >>> path_to_mtf_small_mt5_checkpoint = '<fill_in>'
+        >>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
+        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
+        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path)
+        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
+        """
+
+        model = FlaxMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+        tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+
+        input_ids = tokenizer("Hello there", return_tensors="np").input_ids
+        labels = tokenizer("Hi I am", return_tensors="np").input_ids
+
+        decoder_input_ids = shift_tokens_right(labels, model.config.pad_token_id, model.config.decoder_start_token_id)
+
+        logits = model(input_ids, decoder_input_ids=decoder_input_ids).logits
+        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()
+
+        mtf_score = -(labels.shape[-1] * loss.item())
+
+        EXPECTED_SCORE = -84.9127
+        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

tests/mt5/test_modeling_mt5.py

@@ -0,0 +1,53 @@
+# Copyright 2020 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 unittest
+
+from transformers import is_torch_available
+from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
+
+
+if is_torch_available():
+    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+
+@require_torch
+@require_sentencepiece
+@require_tokenizers
+class MT5IntegrationTest(unittest.TestCase):
+    @slow
+    def test_small_integration_test(self):
+        """
+        For comparision run:
+        >>> import t5  # pip install t5==0.7.1
+        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
+
+        >>> path_to_mtf_small_mt5_checkpoint = '<fill_in>'
+        >>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
+        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
+        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path)
+        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
+        """
+
+        model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small", return_dict=True).to(torch_device)
+        tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+
+        input_ids = tokenizer("Hello there", return_tensors="pt").input_ids
+        labels = tokenizer("Hi I am", return_tensors="pt").input_ids
+
+        loss = model(input_ids.to(torch_device), labels=labels.to(torch_device)).loss
+        mtf_score = -(labels.shape[-1] * loss.item())
+
+        EXPECTED_SCORE = -84.9127
+        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

tests/mt5/test_modeling_tf_mt5.py

@@ -0,0 +1,73 @@
+# coding=utf-8
+# Copyright 2020 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 unittest
+
+from transformers import is_tf_available
+from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import AutoTokenizer, T5Tokenizer, TFAutoModelForSeq2SeqLM, TFMT5ForConditionalGeneration
+
+
+@require_tf
+class TFMT5ModelTest(unittest.TestCase):  # no mixin with common tests -> most cases are already covered in the TF T5
+    @slow
+    def test_resize_embeddings(self):
+        model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
+        original_vocab_size = model.get_input_embeddings().weight.shape[0]
+        # the vocab size is defined in the model config
+        self.assertEqual(original_vocab_size, model.config.vocab_size)
+
+        tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
+        tokenizer.add_special_tokens({"bos_token": "", "eos_token": ""})
+        model._resize_token_embeddings(len(tokenizer))
+        # the vocab size is now resized to the length of the tokenizer, which is different from the original size
+        self.assertEqual(model.get_input_embeddings().weight.shape[0], len(tokenizer))
+        self.assertNotEqual(model.get_input_embeddings().weight.shape[0], original_vocab_size)
+
+
+@require_tf
+@require_sentencepiece
+@require_tokenizers
+class TFMT5ModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_small_integration_test(self):
+        """
+        For comparision run:
+        >>> import t5  # pip install t5==0.7.1
+        >>> from t5.data.sentencepiece_vocabulary import SentencePieceVocabulary
+
+        >>> path_to_mtf_small_mt5_checkpoint = '<fill_in>'
+        >>> path_to_mtf_small_mt5_spm_model_path = '<fill_in>'
+        >>> t5_model = t5.models.MtfModel(model_dir=path_to_mtf_small_mt5_checkpoint, batch_size=1, tpu=None)
+        >>> vocab = SentencePieceVocabulary(path_to_mtf_small_mt5_spm_model_path, extra_ids=100)
+        >>> score = t5_model.score(inputs=["Hello there"], targets=["Hi I am"], vocabulary=vocab)
+        """
+
+        model = TFAutoModelForSeq2SeqLM.from_pretrained("google/mt5-small")
+        tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
+
+        input_ids = tokenizer("Hello there", return_tensors="tf").input_ids
+        labels = tokenizer("Hi I am", return_tensors="tf").input_ids
+
+        loss = model(input_ids, labels=labels).loss
+        mtf_score = -tf.math.reduce_sum(loss).numpy()
+
+        EXPECTED_SCORE = -84.9127
+        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)