TFMarian, TFMbart, TFPegasus, TFBlenderbot (#7987)

* Start plumbing * Marian close * Small stubs for all children * Fixed bart * marian working * pegasus test is good, but failing * Checkin tests * More model files * Subtle marian, pegasus integration test failures * Works well * rm print * boom boom * Still failing model2doc * merge master * Equivalence test failing, all others fixed * cleanup * Fix embed_scale * Cleanup marian pipeline test * Undo extra changes * Smaller delta * Cleanup model testers * undo delta * fix tests import structure * cross test decorator * Cleaner set_weights * Respect authorized_unexpected_keys * No warnings * No warnings * style * Nest tf import * black * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * functional dropout * fixup * Fixup * style_doc * embs * shape list * delete slow force_token_id_to_be_generated func * fixup Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
2020-10-30 11:23:16 -04:00
parent 6279072f5f
commit 566b083eb1
20 changed files with 1063 additions and 106 deletions
--- a/tests/test_modeling_tf_mbart.py
+++ b/tests/test_modeling_tf_mbart.py
@@ -0,0 +1,134 @@
+# coding=utf-8
+# Copyright 2020 HuggingFace Inc. team.
+#
+# 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 tests.test_configuration_common import ConfigTester
+from tests.test_modeling_tf_bart import TFBartModelTester
+from tests.test_modeling_tf_common import TFModelTesterMixin
+from transformers import AutoTokenizer, MBartConfig, is_tf_available
+from transformers.file_utils import cached_property
+from transformers.testing_utils import is_pt_tf_cross_test, require_sentencepiece, require_tf, require_tokenizers, slow
+
+
+if is_tf_available():
+
+    import tensorflow as tf
+
+    from transformers import TFAutoModelForSeq2SeqLM, TFMBartForConditionalGeneration
+
+
+class ModelTester(TFBartModelTester):
+    config_updates = dict(normalize_before=True, add_final_layer_norm=True)
+    config_cls = MBartConfig
+
+
+@require_tf
+class TestTFMBartCommon(TFModelTesterMixin, unittest.TestCase):
+    all_model_classes = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
+    all_generative_model_classes = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
+    model_tester_cls = ModelTester
+    is_encoder_decoder = True
+    test_pruning = False
+
+    def setUp(self):
+        self.model_tester = self.model_tester_cls(self)
+        self.config_tester = ConfigTester(self, config_class=MBartConfig)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_inputs_embeds(self):
+        # inputs_embeds not supported
+        pass
+
+    def test_saved_model_with_hidden_states_output(self):
+        # Should be uncommented during patrick TF refactor
+        pass
+
+    def test_saved_model_with_attentions_output(self):
+        # Should be uncommented during patrick TF refactor
+        pass
+
+    def test_compile_tf_model(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
+        loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+        metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
+
+        model_class = self.all_generative_model_classes[0]
+        input_ids = {
+            "decoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"),
+            "input_ids": tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32"),
+        }
+
+        # Prepare our model
+        model = model_class(config)
+        model(self._prepare_for_class(inputs_dict, model_class))  # Model must be called before saving.
+        # Let's load it from the disk to be sure we can use pretrained weights
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            model = model_class.from_pretrained(tmpdirname)
+
+        outputs_dict = model(input_ids)
+        hidden_states = outputs_dict[0]
+
+        # Add a dense layer on top to test integration with other keras modules
+        outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
+
+        # Compile extended model
+        extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
+        extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
+
+
+@is_pt_tf_cross_test
+@require_sentencepiece
+@require_tokenizers
+class TestMBartEnRO(unittest.TestCase):
+    src_text = [
+        " UN Chief Says There Is No Military Solution in Syria",
+    ]
+    expected_text = [
+        "Şeful ONU declară că nu există o soluţie militară în Siria",
+    ]
+    model_name = "facebook/mbart-large-en-ro"
+
+    @cached_property
+    def tokenizer(self):
+        return AutoTokenizer.from_pretrained(self.model_name)
+
+    @cached_property
+    def model(self):
+        model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True)
+        return model
+
+    def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
+        generated_words = self.translate_src_text(**tokenizer_kwargs)
+        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, return_tensors="tf"
+        )
+        generated_ids = self.model.generate(
+            model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2
+        )
+        generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        return generated_words
+
+    @slow
+    def test_batch_generation_en_ro(self):
+        self._assert_generated_batch_equal_expected()