142 lines
5.5 KiB
Python
142 lines
5.5 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 tempfile
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import unittest
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from transformers import AutoTokenizer, PegasusConfig, is_tf_available
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from transformers.file_utils import cached_property
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from transformers.testing_utils import is_pt_tf_cross_test, require_sentencepiece, require_tf, require_tokenizers, slow
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from .test_configuration_common import ConfigTester
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from .test_modeling_pegasus import PGE_ARTICLE, XSUM_ENTRY_LONGER
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from .test_modeling_tf_bart import TFBartModelTester
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from .test_modeling_tf_common import TFModelTesterMixin
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if is_tf_available():
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import tensorflow as tf
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from transformers import TFAutoModelForSeq2SeqLM, TFPegasusForConditionalGeneration
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class ModelTester(TFBartModelTester):
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config_updates = dict(
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normalize_before=True,
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static_position_embeddings=True,
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)
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hidden_act = "relu"
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config_cls = PegasusConfig
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@require_tf
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class TestTFPegasusCommon(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
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all_generative_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
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model_tester_cls = ModelTester
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is_encoder_decoder = True
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test_pruning = False
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def setUp(self):
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self.model_tester = self.model_tester_cls(self)
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self.config_tester = ConfigTester(self, config_class=PegasusConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_inputs_embeds(self):
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# inputs_embeds not supported
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pass
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def test_saved_model_with_hidden_states_output(self):
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# Should be uncommented during patrick TF refactor
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pass
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def test_saved_model_with_attentions_output(self):
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# Should be uncommented during patrick TF refactor
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pass
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def test_compile_tf_model(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
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loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
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model_class = self.all_generative_model_classes[0]
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input_ids = {
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"decoder_input_ids": tf.keras.Input(batch_shape=(2, 2000), name="decoder_input_ids", dtype="int32"),
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"input_ids": tf.keras.Input(batch_shape=(2, 2000), name="input_ids", dtype="int32"),
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}
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# Prepare our model
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model = model_class(config)
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model(self._prepare_for_class(inputs_dict, model_class)) # Model must be called before saving.
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# Let's load it from the disk to be sure we can use pretrained weights
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(tmpdirname)
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outputs_dict = model(input_ids)
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hidden_states = outputs_dict[0]
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# Add a dense layer on top to test integration with other keras modules
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outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)
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# Compile extended model
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extended_model = tf.keras.Model(inputs=[input_ids], outputs=[outputs])
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extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
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@is_pt_tf_cross_test
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@require_sentencepiece
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@require_tokenizers
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class TFPegasusIntegrationTests(unittest.TestCase):
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src_text = [PGE_ARTICLE, XSUM_ENTRY_LONGER]
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expected_text = [
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"California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to reduce the risk of wildfires.",
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'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
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] # differs slightly from pytorch, likely due to numerical differences in linear layers
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model_name = "google/pegasus-xsum"
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@cached_property
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def tokenizer(self):
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return AutoTokenizer.from_pretrained(self.model_name)
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@cached_property
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def model(self):
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model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True)
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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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assert 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(
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src_texts=self.src_text, **tokenizer_kwargs, return_tensors="tf"
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)
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generated_ids = self.model.generate(
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model_inputs.input_ids,
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attention_mask=model_inputs.attention_mask,
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num_beams=2,
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use_cache=True,
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)
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generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)
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return generated_words
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@slow
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def test_batch_generation(self):
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self._assert_generated_batch_equal_expected()
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