transformers/tests/test_modeling_tf_pegasus.py

# 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 transformers import AutoTokenizer, PegasusConfig, 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

from .test_configuration_common import ConfigTester
from .test_modeling_tf_bart import TFBartModelTester
from .test_modeling_tf_common import TFModelTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeq2SeqLM, TFPegasusForConditionalGeneration


class ModelTester(TFBartModelTester):
    config_updates = dict(
        normalize_before=True,
        static_position_embeddings=True,
    )
    hidden_act = "relu"
    config_cls = PegasusConfig


@require_tf
class TestTFPegasusCommon(TFModelTesterMixin, unittest.TestCase):
    all_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    all_generative_model_classes = (TFPegasusForConditionalGeneration,) 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=PegasusConfig)

    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])

    def test_model_common_attributes(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
            x = model.get_output_layer_with_bias()
            assert x is None
            name = model.get_prefix_bias_name()
            assert name is None


@is_pt_tf_cross_test
@require_sentencepiece
@require_tokenizers
class TFPegasusIntegrationTests(unittest.TestCase):
    src_text = [
        """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
        """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning 'Oh I think you're nominated'", said Dappy."And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around."At the end of the day we're grateful to be where we are in our careers."If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" """,
    ]
    expected_text = [
        "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to reduce the risk of wildfires.",
        'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
    ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
    model_name = "google/pegasus-xsum"

    @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)
        assert 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,
            use_cache=True,
        )
        generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)
        return generated_words

    @slow
    def test_batch_generation(self):
        self._assert_generated_batch_equal_expected()