transformers/tests/models/marian/test_modeling_flax_marian.py

# 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

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"

    import jax
    import jax.numpy as jnp

    from transformers import MarianTokenizer
    from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right


def prepare_marian_inputs_dict(
    config,
    input_ids,
    decoder_input_ids=None,
    attention_mask=None,
    decoder_attention_mask=None,
    head_mask=None,
    decoder_head_mask=None,
    cross_attn_head_mask=None,
):
    if attention_mask is None:
        attention_mask = np.where(input_ids != config.pad_token_id, 1, 0)
    if decoder_attention_mask is None:
        decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0)
    if head_mask is None:
        head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads))
    if decoder_head_mask is None:
        decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
    if cross_attn_head_mask is None:
        cross_attn_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads))
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }


class FlaxMarianModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=16,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=4,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=32,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
        initializer_range=0.02,
    ):
        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_act = hidden_act
        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
        self.initializer_range = initializer_range

    def prepare_config_and_inputs(self):
        input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size)
        input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1)

        decoder_input_ids = shift_tokens_right(input_ids, 1, 2)

        config = MarianConfig(
            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_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
            initializer_range=self.initializer_range,
            use_cache=False,
        )
        inputs_dict = prepare_marian_inputs_dict(config, input_ids, decoder_input_ids)
        return config, inputs_dict

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict

    def check_use_cache_forward(self, model_class_name, config, inputs_dict):
        max_decoder_length = 20
        model = model_class_name(config)

        encoder_outputs = model.encode(inputs_dict["input_ids"])

        decoder_input_ids, decoder_attention_mask = (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
        decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")

        decoder_position_ids = jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
            (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
        )
        outputs_cache = model.decode(
            decoder_input_ids[:, :-1],
            encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            past_key_values=past_key_values,
            decoder_position_ids=decoder_position_ids,
        )

        decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
        outputs_cache_next = model.decode(
            decoder_input_ids[:, -1:],
            encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            past_key_values=outputs_cache.past_key_values,
            decoder_position_ids=decoder_position_ids,
        )

        outputs = model.decode(decoder_input_ids, encoder_outputs)

        diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")

    def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
        max_decoder_length = 20
        model = model_class_name(config)

        encoder_outputs = model.encode(inputs_dict["input_ids"])

        decoder_input_ids, decoder_attention_mask = (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        decoder_attention_mask_cache = jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
            ],
            axis=-1,
        )

        past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
        decoder_position_ids = jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
            (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
        )

        outputs_cache = model.decode(
            decoder_input_ids[:, :-1],
            encoder_outputs,
            decoder_attention_mask=decoder_attention_mask_cache,
            past_key_values=past_key_values,
            decoder_position_ids=decoder_position_ids,
        )
        decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
        outputs_cache_next = model.decode(
            decoder_input_ids[:, -1:],
            encoder_outputs,
            past_key_values=outputs_cache.past_key_values,
            decoder_attention_mask=decoder_attention_mask_cache,
            decoder_position_ids=decoder_position_ids,
        )

        outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)

        diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
        self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")


@require_flax
class FlaxMarianModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin):
    is_encoder_decoder = True
    all_model_classes = (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
    all_generative_model_classes = (FlaxMarianMTModel,) if is_flax_available() else ()

    def setUp(self):
        self.model_tester = FlaxMarianModelTester(self)

    def test_use_cache_forward(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)

    def test_use_cache_forward_with_attn_mask(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)

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

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
                model = model_class(config)

                @jax.jit
                def encode_jitted(input_ids, attention_mask=None, **kwargs):
                    return model.encode(input_ids=input_ids, attention_mask=attention_mask)

                with self.subTest("JIT Enabled"):
                    jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()

                with self.subTest("JIT Disabled"):
                    with jax.disable_jit():
                        outputs = encode_jitted(**prepared_inputs_dict).to_tuple()

                self.assertEqual(len(outputs), len(jitted_outputs))
                for jitted_output, output in zip(jitted_outputs, outputs):
                    self.assertEqual(jitted_output.shape, output.shape)

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

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                model = model_class(config)
                encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])

                prepared_inputs_dict = {
                    "decoder_input_ids": inputs_dict["decoder_input_ids"],
                    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                    "encoder_outputs": encoder_outputs,
                }

                @jax.jit
                def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
                    return model.decode(
                        decoder_input_ids=decoder_input_ids,
                        decoder_attention_mask=decoder_attention_mask,
                        encoder_outputs=encoder_outputs,
                    )

                with self.subTest("JIT Enabled"):
                    jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()

                with self.subTest("JIT Disabled"):
                    with jax.disable_jit():
                        outputs = decode_jitted(**prepared_inputs_dict).to_tuple()

                self.assertEqual(len(outputs), len(jitted_outputs))
                for jitted_output, output in zip(jitted_outputs, outputs):
                    self.assertEqual(jitted_output.shape, output.shape)

    @slow
    def test_model_from_pretrained(self):
        for model_class_name in self.all_model_classes:
            model = model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de")
            # FlaxMarianForSequenceClassification expects eos token in input_ids
            input_ids = np.ones((1, 1)) * model.config.eos_token_id
            outputs = model(input_ids)
            self.assertIsNotNone(outputs)


@require_flax
@require_sentencepiece
@require_tokenizers
class MarianIntegrationTest(unittest.TestCase):
    src = None
    tgt = None

    @classmethod
    def setUpClass(cls) -> None:
        cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
        return cls

    @cached_property
    def tokenizer(self):
        return MarianTokenizer.from_pretrained(self.model_name)

    @property
    def eos_token_id(self) -> int:
        return self.tokenizer.eos_token_id

    @cached_property
    def model(self):
        model: FlaxMarianMTModel = FlaxMarianMTModel.from_pretrained(self.model_name)
        self.assertEqual(model.config.decoder_start_token_id, model.config.pad_token_id)
        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(self.src_text, padding=True, return_tensors="np", **tokenizer_kwargs)
        generated_ids = self.model.generate(
            model_inputs.input_ids,
            attention_mask=model_inputs.attention_mask,
            num_beams=2,
            max_length=128,
        ).sequences
        generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
        return generated_words


@require_flax
@require_sentencepiece
@require_tokenizers
class TestMarian_EN_FR(MarianIntegrationTest):
    src = "en"
    tgt = "fr"
    src_text = [
        "I am a small frog.",
        "Now I can forget the 100 words of german that I know.",
    ]
    expected_text = [
        "Je suis une petite grenouille.",
        "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
    ]

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


@require_flax
@require_sentencepiece
@require_tokenizers
class TestMarian_FR_EN(MarianIntegrationTest):
    src = "fr"
    tgt = "en"
    src_text = [
        "Donnez moi le micro.",
        "Tom et Mary étaient assis à une table.",  # Accents
    ]
    expected_text = [
        "Give me the microphone.",
        "Tom and Mary were sitting at a table.",
    ]

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


@require_flax
@require_sentencepiece
@require_tokenizers
class TestMarian_MT_EN(MarianIntegrationTest):
    """Cover low resource/high perplexity setting. This breaks without adjust_logits_generation overwritten"""

    src = "mt"
    tgt = "en"
    src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
    expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]

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


@require_flax
@require_sentencepiece
@require_tokenizers
class TestMarian_EN_DE(MarianIntegrationTest):
    src = "en"
    tgt = "de"
    src_text = [
        "I am a small frog.",
        "Now I can forget the 100 words of german that I know.",
        "Tom asked his teacher for advice.",
        "That's how I would do it.",
        "Tom really admired Mary's courage.",
        "Turn around and close your eyes.",
    ]
    expected_text = [
        "Ich bin ein kleiner Frosch.",
        "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
        "Tom bat seinen Lehrer um Rat.",
        "So würde ich das machen.",
        "Tom bewunderte Marias Mut wirklich.",
        "Drehen Sie sich um und schließen Sie die Augen.",
    ]

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


@require_flax
@require_sentencepiece
@require_tokenizers
class TestMarian_en_zh(MarianIntegrationTest):
    src = "en"
    tgt = "zh"
    src_text = ["My name is Wolfgang and I live in Berlin"]
    expected_text = ["我叫沃尔夫冈 我住在柏林"]

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


@require_flax
@require_sentencepiece
@require_tokenizers
class TestMarian_RU_FR(MarianIntegrationTest):
    src = "ru"
    tgt = "fr"
    src_text = ["Он показал мне рукопись своей новой пьесы."]
    expected_text = ["Il m'a montré le manuscrit de sa nouvelle pièce."]

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


@require_flax
@require_sentencepiece
@require_tokenizers
class TestMarian_en_ROMANCE(MarianIntegrationTest):
    """Multilingual on target side."""

    src = "en"
    tgt = "ROMANCE"
    src_text = [
        ">>fr<< Don't spend so much time watching TV.",
        ">>pt<< Your message has been sent.",
        ">>es<< He's two years older than me.",
    ]
    expected_text = [
        "Ne passez pas autant de temps à regarder la télé.",
        "A sua mensagem foi enviada.",
        "Es dos años más viejo que yo.",
    ]

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