test_modeling_tf_pegasus.py

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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 tempfile
import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.file_utils import cached_property
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeq2SeqLM, TFPegasusForConditionalGeneration, TFPegasusModel


@require_tf
class TFPegasusModelTester:
    config_cls = PegasusConfig
    config_updates = {}
    hidden_act = "gelu"

    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=5,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=20,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size

        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id

    def prepare_config_and_inputs_for_common(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
        eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
        input_ids = tf.concat([input_ids, eos_tensor], axis=1)

        decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        config = self.config_cls(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            encoder_layers=self.num_hidden_layers,
            decoder_layers=self.num_hidden_layers,
            encoder_attention_heads=self.num_attention_heads,
            decoder_attention_heads=self.num_attention_heads,
            encoder_ffn_dim=self.intermediate_size,
            decoder_ffn_dim=self.intermediate_size,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            eos_token_ids=[2],
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            decoder_start_token_id=self.pad_token_id,
            **self.config_updates,
        )
        inputs_dict = prepare_pegasus_inputs_dict(config, input_ids, decoder_input_ids)
        return config, inputs_dict

    def check_decoder_model_past_large_inputs(self, config, inputs_dict):
        model = TFPegasusModel(config=config).get_decoder()
        input_ids = inputs_dict["input_ids"]

        input_ids = input_ids[:1, :]
        attention_mask = inputs_dict["attention_mask"][:1, :]
        self.batch_size = 1

        # first forward pass
        outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)

        output, past_key_values = outputs.to_tuple()
        past_key_values = past_key_values[1]

        # create hypothetical next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
        next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8)

        # append to next input_ids and
        next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
        next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)

        output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
        output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]

        self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])

        # select random slice
        random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
        output_from_past_slice = output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)


def prepare_pegasus_inputs_dict(
    config,
    input_ids,
    decoder_input_ids,
    attention_mask=None,
    decoder_attention_mask=None,
):
    if attention_mask is None:
        attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
    if decoder_attention_mask is None:
        decoder_attention_mask = tf.concat(
            [
                tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
                tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
            ],
            axis=-1,
        )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }


@require_tf
class TFPegasusModelTest(TFModelTesterMixin, unittest.TestCase):
    all_model_classes = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    all_generative_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    is_encoder_decoder = True
    test_pruning = False

    def setUp(self):
        self.model_tester = TFPegasusModelTester(self)
        self.config_tester = ConfigTester(self, config_class=PegasusConfig)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_decoder_model_past_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)

    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)

            if model_class in self.all_generative_model_classes:
                x = model.get_output_embeddings()
                assert isinstance(x, tf.keras.layers.Layer)
                name = model.get_bias()
                assert isinstance(name, dict)
                for k, v in name.items():
                    assert isinstance(v, tf.Variable)
            else:
                x = model.get_output_embeddings()
                assert x is None
                name = model.get_bias()
                assert name is None

    def test_saved_model_creation(self):
        # This test is too long (>30sec) and makes fail the CI
        pass

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

        def _get_word_embedding_weight(model, embedding_layer):
            if hasattr(embedding_layer, "weight"):
                return embedding_layer.weight
            else:
                # Here we build the word embeddings weights if not exists.
                # And then we retry to get the attribute once built.
                model(model.dummy_inputs)
                if hasattr(embedding_layer, "weight"):
                    return embedding_layer.weight
                else:
                    return None

        for model_class in self.all_model_classes:
            for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
                # build the embeddings
                model = model_class(config=config)
                old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
                old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
                old_final_logits_bias = model.get_bias()

                # reshape the embeddings
                model.resize_token_embeddings(size)
                new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
                new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
                new_final_logits_bias = model.get_bias()

                # check that the resized embeddings size matches the desired size.
                assert_size = size if size is not None else config.vocab_size

                self.assertEqual(new_input_embeddings.shape[0], assert_size)

                # check that weights remain the same after resizing
                models_equal = True
                for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()):
                    if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                        models_equal = False
                self.assertTrue(models_equal)

                if old_output_embeddings is not None and new_output_embeddings is not None:
                    self.assertEqual(new_output_embeddings.shape[0], assert_size)

                    models_equal = True
                    for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()):
                        if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                            models_equal = False
                    self.assertTrue(models_equal)

                if old_final_logits_bias is not None and new_final_logits_bias is not None:
                    old_final_logits_bias = old_final_logits_bias["final_logits_bias"]
                    new_final_logits_bias = new_final_logits_bias["final_logits_bias"]
                    self.assertEqual(new_final_logits_bias.shape[0], 1)
                    self.assertEqual(new_final_logits_bias.shape[1], assert_size)

                    models_equal = True
                    for old, new in zip(old_final_logits_bias.value(), new_final_logits_bias.value()):
                        for p1, p2 in zip(old, new):
                            if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                                models_equal = False
                    self.assertTrue(models_equal)


def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
    """If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
    if a is None and b is None:
        return True
    try:
        if tf.debugging.assert_near(a, b, atol=atol):
            return True
        raise
    except Exception:
        msg = "{} != {}".format(a, b)
        if prefix:
            msg = prefix + ": " + msg
        raise AssertionError(msg)


def _long_tensor(tok_lst):
    return tf.constant(tok_lst, dtype=tf.int32)


@require_sentencepiece
@require_tokenizers
@require_tf
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)
        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()