test_modeling_fsmt.py

# coding=utf-8
# Copyright 2020 Huggingface
#
# 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

import timeout_decorator  # noqa

from parameterized import parameterized
from transformers import is_torch_available
from transformers.file_utils import cached_property
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from .test_configuration_common import ConfigTester
from .test_generation_utils import GenerationTesterMixin
from .test_modeling_common import ModelTesterMixin, ids_tensor


if is_torch_available():
    import torch

    from transformers import FSMTConfig, FSMTForConditionalGeneration, FSMTModel, FSMTTokenizer
    from transformers.models.fsmt.modeling_fsmt import (
        SinusoidalPositionalEmbedding,
        _prepare_fsmt_decoder_inputs,
        invert_mask,
        shift_tokens_right,
    )
    from transformers.pipelines import TranslationPipeline


@require_torch
class ModelTester:
    def __init__(
        self,
        parent,
    ):
        self.parent = parent
        self.src_vocab_size = 99
        self.tgt_vocab_size = 99
        self.langs = ["ru", "en"]
        self.batch_size = 13
        self.seq_length = 7
        self.is_training = False
        self.use_labels = False
        self.hidden_size = 16
        self.num_hidden_layers = 2
        self.num_attention_heads = 4
        self.intermediate_size = 4
        self.hidden_act = "relu"
        self.hidden_dropout_prob = 0.1
        self.attention_probs_dropout_prob = 0.1
        self.max_position_embeddings = 20
        self.bos_token_id = 0
        self.pad_token_id = 1
        self.eos_token_id = 2
        torch.manual_seed(0)

        # hack needed for modeling_common tests - despite not really having this attribute in this model
        self.vocab_size = self.src_vocab_size

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.src_vocab_size).clamp(
            3,
        )
        input_ids[:, -1] = 2  # Eos Token

        config = FSMTConfig(
            vocab_size=self.src_vocab_size,  # hack needed for common tests
            src_vocab_size=self.src_vocab_size,
            tgt_vocab_size=self.tgt_vocab_size,
            langs=self.langs,
            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,
        )
        inputs_dict = prepare_fsmt_inputs_dict(config, input_ids)
        return config, inputs_dict

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"]
        inputs_dict["decoder_attention_mask"] = inputs_dict["attention_mask"]
        inputs_dict["use_cache"] = False
        return config, inputs_dict


def prepare_fsmt_inputs_dict(
    config,
    input_ids,
    attention_mask=None,
    head_mask=None,
    decoder_head_mask=None,
    cross_attn_head_mask=None,
):
    if attention_mask is None:
        attention_mask = input_ids.ne(config.pad_token_id)
    if head_mask is None:
        head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
    if decoder_head_mask is None:
        decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
    if cross_attn_head_mask is None:
        cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
    }


@require_torch
class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    all_model_classes = (FSMTModel, FSMTForConditionalGeneration) if is_torch_available() else ()
    all_generative_model_classes = (FSMTForConditionalGeneration,) if is_torch_available() else ()
    is_encoder_decoder = True
    test_pruning = False
    test_missing_keys = False

    def setUp(self):
        self.model_tester = ModelTester(self)
        self.langs = ["en", "ru"]
        config = {
            "langs": self.langs,
            "src_vocab_size": 10,
            "tgt_vocab_size": 20,
        }
        # XXX: hack to appease to all other models requiring `vocab_size`
        config["vocab_size"] = 99  # no such thing in FSMT
        self.config_tester = ConfigTester(self, config_class=FSMTConfig, **config)

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

    # XXX: override test_model_common_attributes / different Embedding type
    def test_model_common_attributes(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Embedding))
            model.set_input_embeddings(torch.nn.Embedding(10, 10))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, torch.nn.modules.sparse.Embedding))

    def test_initialization_more(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        model = FSMTModel(config)
        model.to(torch_device)
        model.eval()
        # test init
        # self.assertTrue((model.encoder.embed_tokens.weight == model.shared.weight).all().item())

        def _check_var(module):
            """Check that we initialized various parameters from N(0, config.init_std)."""
            self.assertAlmostEqual(torch.std(module.weight).item(), config.init_std, 2)

        _check_var(model.encoder.embed_tokens)
        _check_var(model.encoder.layers[0].self_attn.k_proj)
        _check_var(model.encoder.layers[0].fc1)
        # XXX: different std for fairseq version of SinusoidalPositionalEmbedding
        # self.assertAlmostEqual(torch.std(model.encoder.embed_positions.weights).item(), config.init_std, 2)

    def test_advanced_inputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        config.use_cache = False
        inputs_dict["input_ids"][:, -2:] = config.pad_token_id
        decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs(
            config, inputs_dict["input_ids"]
        )
        model = FSMTModel(config).to(torch_device).eval()

        decoder_features_with_created_mask = model(**inputs_dict)[0]
        decoder_features_with_passed_mask = model(
            decoder_attention_mask=invert_mask(decoder_attn_mask), decoder_input_ids=decoder_input_ids, **inputs_dict
        )[0]
        _assert_tensors_equal(decoder_features_with_passed_mask, decoder_features_with_created_mask)
        useless_mask = torch.zeros_like(decoder_attn_mask)
        decoder_features = model(decoder_attention_mask=useless_mask, **inputs_dict)[0]
        self.assertTrue(isinstance(decoder_features, torch.Tensor))  # no hidden states or attentions
        self.assertEqual(
            decoder_features.size(),
            (self.model_tester.batch_size, self.model_tester.seq_length, config.tgt_vocab_size),
        )
        if decoder_attn_mask.min().item() < -1e3:  # some tokens were masked
            self.assertFalse((decoder_features_with_created_mask == decoder_features).all().item())

        # Test different encoder attention masks
        decoder_features_with_long_encoder_mask = model(
            inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"].long()
        )[0]
        _assert_tensors_equal(decoder_features_with_long_encoder_mask, decoder_features_with_created_mask)

    def test_save_load_missing_keys(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()

        for model_class in self.all_model_classes:
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
            self.assertEqual(info["missing_keys"], [])

    @unittest.skip("Test has a segmentation fault on torch 1.8.0")
    def test_export_to_onnx(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        model = FSMTModel(config).to(torch_device)
        with tempfile.TemporaryDirectory() as tmpdirname:
            torch.onnx.export(
                model,
                (inputs_dict["input_ids"], inputs_dict["attention_mask"]),
                f"{tmpdirname}/fsmt_test.onnx",
                export_params=True,
                opset_version=12,
                input_names=["input_ids", "attention_mask"],
            )

    @unittest.skip("can't be implemented for FSMT due to dual vocab.")
    def test_resize_tokens_embeddings(self):
        pass

    @unittest.skip("Passing inputs_embeds not implemented for FSMT.")
    def test_inputs_embeds(self):
        pass

    @unittest.skip("model weights aren't tied in FSMT.")
    def test_tie_model_weights(self):
        pass

    @unittest.skip("TODO: Decoder embeddings cannot be resized at the moment")
    def test_resize_embeddings_untied(self):
        pass


@require_torch
class FSMTHeadTests(unittest.TestCase):
    src_vocab_size = 99
    tgt_vocab_size = 99
    langs = ["ru", "en"]

    def _get_config(self):
        return FSMTConfig(
            src_vocab_size=self.src_vocab_size,
            tgt_vocab_size=self.tgt_vocab_size,
            langs=self.langs,
            d_model=24,
            encoder_layers=2,
            decoder_layers=2,
            encoder_attention_heads=2,
            decoder_attention_heads=2,
            encoder_ffn_dim=32,
            decoder_ffn_dim=32,
            max_position_embeddings=48,
            eos_token_id=2,
            pad_token_id=1,
            bos_token_id=0,
        )

    def _get_config_and_data(self):
        input_ids = torch.tensor(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ],
            dtype=torch.long,
            device=torch_device,
        )

        batch_size = input_ids.shape[0]
        config = self._get_config()
        return config, input_ids, batch_size

    def test_generate_beam_search(self):
        input_ids = torch.tensor([[71, 82, 2], [68, 34, 2]], dtype=torch.long, device=torch_device)
        config = self._get_config()
        lm_model = FSMTForConditionalGeneration(config).to(torch_device)
        lm_model.eval()

        max_length = 5
        new_input_ids = lm_model.generate(
            input_ids.clone(),
            do_sample=True,
            num_return_sequences=1,
            num_beams=2,
            no_repeat_ngram_size=3,
            max_length=max_length,
        )
        self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length))

    def test_shift_tokens_right(self):
        input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long)
        shifted = shift_tokens_right(input_ids, 1)
        n_pad_before = input_ids.eq(1).float().sum()
        n_pad_after = shifted.eq(1).float().sum()
        self.assertEqual(shifted.shape, input_ids.shape)
        self.assertEqual(n_pad_after, n_pad_before - 1)
        self.assertTrue(torch.eq(shifted[:, 0], 2).all())

    def test_generate_fp16(self):
        config, input_ids, batch_size = self._get_config_and_data()
        attention_mask = input_ids.ne(1).to(torch_device)
        model = FSMTForConditionalGeneration(config).eval().to(torch_device)
        if torch_device == "cuda":
            model.half()
        model.generate(input_ids, attention_mask=attention_mask)
        model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)

    def test_dummy_inputs(self):
        config, *_ = self._get_config_and_data()
        model = FSMTForConditionalGeneration(config).eval().to(torch_device)
        model(**model.dummy_inputs)

    def test_prepare_fsmt_decoder_inputs(self):
        config, *_ = self._get_config_and_data()
        input_ids = _long_tensor(([4, 4, 2]))
        decoder_input_ids = _long_tensor([[26388, 2, config.pad_token_id]])
        ignore = float("-inf")
        decoder_input_ids, decoder_attn_mask, causal_mask = _prepare_fsmt_decoder_inputs(
            config, input_ids, decoder_input_ids
        )
        expected_causal_mask = torch.tensor(
            [[0, ignore, ignore], [0, 0, ignore], [0, 0, 0]]  # never attend to the final token, because its pad
        ).to(input_ids.device)
        self.assertEqual(decoder_attn_mask.size(), decoder_input_ids.size())
        self.assertTrue(torch.eq(expected_causal_mask, causal_mask).all())


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 torch.allclose(a, b, atol=atol):
            return True
        raise
    except Exception:
        if len(prefix) > 0:
            prefix = f"{prefix}: "
        raise AssertionError(f"{prefix}{a} != {b}")


def _long_tensor(tok_lst):
    return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)


TOLERANCE = 1e-4


pairs = [
    ["en-ru"],
    ["ru-en"],
    ["en-de"],
    ["de-en"],
]


@require_torch
@require_sentencepiece
@require_tokenizers
class FSMTModelIntegrationTests(unittest.TestCase):
    tokenizers_cache = {}
    models_cache = {}
    default_mname = "facebook/wmt19-en-ru"

    @cached_property
    def default_tokenizer(self):
        return self.get_tokenizer(self.default_mname)

    @cached_property
    def default_model(self):
        return self.get_model(self.default_mname)

    def get_tokenizer(self, mname):
        if mname not in self.tokenizers_cache:
            self.tokenizers_cache[mname] = FSMTTokenizer.from_pretrained(mname)
        return self.tokenizers_cache[mname]

    def get_model(self, mname):
        if mname not in self.models_cache:
            self.models_cache[mname] = FSMTForConditionalGeneration.from_pretrained(mname).to(torch_device)
            if torch_device == "cuda":
                self.models_cache[mname].half()
        return self.models_cache[mname]

    @slow
    def test_inference_no_head(self):
        tokenizer = self.default_tokenizer
        model = FSMTModel.from_pretrained(self.default_mname).to(torch_device)

        src_text = "My friend computer will translate this for me"
        input_ids = tokenizer([src_text], return_tensors="pt")["input_ids"]
        input_ids = _long_tensor(input_ids).to(torch_device)
        inputs_dict = prepare_fsmt_inputs_dict(model.config, input_ids)
        with torch.no_grad():
            output = model(**inputs_dict)[0]
        expected_shape = torch.Size((1, 10, model.config.tgt_vocab_size))
        self.assertEqual(output.shape, expected_shape)
        # expected numbers were generated when en-ru model, using just fairseq's model4.pt
        # may have to adjust if switched to a different checkpoint
        expected_slice = torch.tensor(
            [[-1.5753, -1.5753, 2.8975], [-0.9540, -0.9540, 1.0299], [-3.3131, -3.3131, 0.5219]]
        ).to(torch_device)
        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))

    def translation_setup(self, pair):
        text = {
            "en": "Machine learning is great, isn't it?",
            "ru": "Машинное обучение - это здорово, не так ли?",
            "de": "Maschinelles Lernen ist großartig, oder?",
        }

        src, tgt = pair.split("-")
        print(f"Testing {src} -> {tgt}")
        mname = f"facebook/wmt19-{pair}"

        src_text = text[src]
        tgt_text = text[tgt]

        tokenizer = self.get_tokenizer(mname)
        model = self.get_model(mname)
        return tokenizer, model, src_text, tgt_text

    @parameterized.expand(pairs)
    @slow
    def test_translation_direct(self, pair):
        tokenizer, model, src_text, tgt_text = self.translation_setup(pair)

        input_ids = tokenizer.encode(src_text, return_tensors="pt").to(torch_device)

        outputs = model.generate(input_ids)
        decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
        assert decoded == tgt_text, f"\n\ngot: {decoded}\nexp: {tgt_text}\n"

    @parameterized.expand(pairs)
    @slow
    def test_translation_pipeline(self, pair):
        tokenizer, model, src_text, tgt_text = self.translation_setup(pair)
        device = 0 if torch_device == "cuda" else -1
        pipeline = TranslationPipeline(model, tokenizer, framework="pt", device=device)
        output = pipeline([src_text])
        self.assertEqual([tgt_text], [x["translation_text"] for x in output])


@require_torch
class TestSinusoidalPositionalEmbeddings(unittest.TestCase):
    padding_idx = 1
    tolerance = 1e-4

    def test_basic(self):
        input_ids = torch.tensor([[4, 10]], dtype=torch.long, device=torch_device)
        emb1 = SinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6, padding_idx=self.padding_idx).to(
            torch_device
        )
        emb = emb1(input_ids)
        desired_weights = torch.tensor(
            [
                [9.0930e-01, 1.9999e-02, 2.0000e-04, -4.1615e-01, 9.9980e-01, 1.0000e00],
                [1.4112e-01, 2.9995e-02, 3.0000e-04, -9.8999e-01, 9.9955e-01, 1.0000e00],
            ]
        ).to(torch_device)
        self.assertTrue(
            torch.allclose(emb[0], desired_weights, atol=self.tolerance),
            msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n",
        )

    def test_odd_embed_dim(self):
        # odd embedding_dim  is allowed
        SinusoidalPositionalEmbedding(num_positions=4, embedding_dim=5, padding_idx=self.padding_idx).to(torch_device)

        # odd num_embeddings is allowed
        SinusoidalPositionalEmbedding(num_positions=5, embedding_dim=4, padding_idx=self.padding_idx).to(torch_device)

    @unittest.skip("different from marian (needs more research)")
    def test_positional_emb_weights_against_marian(self):

        desired_weights = torch.tensor(
            [
                [0, 0, 0, 0, 0],
                [0.84147096, 0.82177866, 0.80180490, 0.78165019, 0.76140374],
                [0.90929741, 0.93651021, 0.95829457, 0.97505713, 0.98720258],
            ]
        )
        emb1 = SinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512, padding_idx=self.padding_idx).to(
            torch_device
        )
        weights = emb1.weights.data[:3, :5]
        # XXX: only the 1st and 3rd lines match - this is testing against
        # verbatim copy of SinusoidalPositionalEmbedding from fairseq
        self.assertTrue(
            torch.allclose(weights, desired_weights, atol=self.tolerance),
            msg=f"\nexp:\n{desired_weights}\ngot:\n{weights}\n",
        )

        # test that forward pass is just a lookup, there is no ignore padding logic
        input_ids = torch.tensor(
            [[4, 10, self.padding_idx, self.padding_idx, self.padding_idx]], dtype=torch.long, device=torch_device
        )
        no_cache_pad_zero = emb1(input_ids)[0]
        # XXX: only the 1st line matches the 3rd
        self.assertTrue(
            torch.allclose(torch.tensor(desired_weights, device=torch_device), no_cache_pad_zero[:3, :5], atol=1e-3)
        )