test_modeling_transfo_xl.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 copy
import random
import unittest

from transformers import is_torch_available

from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .utils import require_multigpu, require_torch, slow, torch_device


if is_torch_available():
    import torch
    from transformers import TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel
    from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST


@require_torch
class TransfoXLModelTest(ModelTesterMixin, unittest.TestCase):

    all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
    all_generative_model_classes = (TransfoXLLMHeadModel,) if is_torch_available() else ()
    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = True

    class TransfoXLModelTester(object):
        def __init__(
            self,
            parent,
            batch_size=14,
            seq_length=7,
            mem_len=30,
            clamp_len=15,
            is_training=True,
            use_labels=True,
            vocab_size=99,
            cutoffs=[10, 50, 80],
            hidden_size=32,
            d_embed=32,
            num_attention_heads=4,
            d_head=8,
            d_inner=128,
            div_val=2,
            num_hidden_layers=5,
            scope=None,
            seed=1,
            eos_token_id=0,
        ):
            self.parent = parent
            self.batch_size = batch_size
            self.seq_length = seq_length
            self.mem_len = mem_len
            self.key_length = seq_length + mem_len
            self.clamp_len = clamp_len
            self.is_training = is_training
            self.use_labels = use_labels
            self.vocab_size = vocab_size
            self.cutoffs = cutoffs
            self.hidden_size = hidden_size
            self.d_embed = d_embed
            self.num_attention_heads = num_attention_heads
            self.d_head = d_head
            self.d_inner = d_inner
            self.div_val = div_val
            self.num_hidden_layers = num_hidden_layers
            self.scope = scope
            self.seed = seed
            self.eos_token_id = eos_token_id

        def prepare_config_and_inputs(self):
            input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
            input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

            lm_labels = None
            if self.use_labels:
                lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

            config = TransfoXLConfig(
                vocab_size=self.vocab_size,
                mem_len=self.mem_len,
                clamp_len=self.clamp_len,
                cutoffs=self.cutoffs,
                d_model=self.hidden_size,
                d_embed=self.d_embed,
                n_head=self.num_attention_heads,
                d_head=self.d_head,
                d_inner=self.d_inner,
                div_val=self.div_val,
                n_layer=self.num_hidden_layers,
                eos_token_id=self.eos_token_id,
            )

            return (config, input_ids_1, input_ids_2, lm_labels)

        def set_seed(self):
            random.seed(self.seed)
            torch.manual_seed(self.seed)

        def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
            model = TransfoXLModel(config)
            model.to(torch_device)
            model.eval()

            hidden_states_1, mems_1 = model(input_ids_1)
            hidden_states_2, mems_2 = model(input_ids_2, mems_1)
            outputs = {
                "hidden_states_1": hidden_states_1,
                "mems_1": mems_1,
                "hidden_states_2": hidden_states_2,
                "mems_2": mems_2,
            }
            return outputs

        def check_transfo_xl_model_output(self, result):
            self.parent.assertListEqual(
                list(result["hidden_states_1"].size()), [self.batch_size, self.seq_length, self.hidden_size],
            )
            self.parent.assertListEqual(
                list(result["hidden_states_2"].size()), [self.batch_size, self.seq_length, self.hidden_size],
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_1"]),
                [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_2"]),
                [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
            )

        def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
            model = TransfoXLLMHeadModel(config)
            model.to(torch_device)
            model.eval()

            lm_logits_1, mems_1 = model(input_ids_1)
            loss_1, _, mems_1 = model(input_ids_1, labels=lm_labels)
            lm_logits_2, mems_2 = model(input_ids_2, mems=mems_1)
            loss_2, _, mems_2 = model(input_ids_2, labels=lm_labels, mems=mems_1)

            outputs = {
                "loss_1": loss_1,
                "mems_1": mems_1,
                "lm_logits_1": lm_logits_1,
                "loss_2": loss_2,
                "mems_2": mems_2,
                "lm_logits_2": lm_logits_2,
            }
            return outputs

        def check_transfo_xl_lm_head_output(self, result):
            self.parent.assertListEqual(list(result["loss_1"].size()), [self.batch_size, self.seq_length - 1])
            self.parent.assertListEqual(
                list(result["lm_logits_1"].size()), [self.batch_size, self.seq_length, self.vocab_size],
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_1"]),
                [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
            )

            self.parent.assertListEqual(list(result["loss_2"].size()), [self.batch_size, self.seq_length - 1])
            self.parent.assertListEqual(
                list(result["lm_logits_2"].size()), [self.batch_size, self.seq_length, self.vocab_size],
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_2"]),
                [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
            )

        def prepare_config_and_inputs_for_common(self):
            config_and_inputs = self.prepare_config_and_inputs()
            (config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
            inputs_dict = {"input_ids": input_ids_1}
            return config, inputs_dict

    def check_cutoffs_and_n_token(
        self, copied_cutoffs, layer, model_embed, model, model_class, resized_value, vocab_size
    ):
        # Check that the cutoffs were modified accordingly
        for i in range(len(copied_cutoffs)):
            if i < layer:
                self.assertEqual(model_embed.cutoffs[i], copied_cutoffs[i])
                if model_class == TransfoXLLMHeadModel:
                    self.assertEqual(model.crit.cutoffs[i], copied_cutoffs[i])
                if i < len(model.config.cutoffs):
                    self.assertEqual(model.config.cutoffs[i], copied_cutoffs[i])
            else:
                self.assertEqual(model_embed.cutoffs[i], copied_cutoffs[i] + resized_value)
                if model_class == TransfoXLLMHeadModel:
                    self.assertEqual(model.crit.cutoffs[i], copied_cutoffs[i] + resized_value)
                if i < len(model.config.cutoffs):
                    self.assertEqual(model.config.cutoffs[i], copied_cutoffs[i] + resized_value)

        self.assertEqual(model_embed.n_token, vocab_size + resized_value)
        if model_class == TransfoXLLMHeadModel:
            self.assertEqual(model.crit.n_token, vocab_size + resized_value)

    def setUp(self):
        self.model_tester = TransfoXLModelTest.TransfoXLModelTester(self)
        self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)

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

    def test_transfo_xl_model(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        output_result = self.model_tester.create_transfo_xl_model(*config_and_inputs)
        self.model_tester.check_transfo_xl_model_output(output_result)

    def test_transfo_xl_lm_head(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
        self.model_tester.check_transfo_xl_lm_head_output(output_result)

    @require_multigpu
    def test_multigpu_data_parallel_forward(self):
        # Opt-out of this test.
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_name in TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = TransfoXLModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    def test_resize_tokens_embeddings(self):
        (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
        if not self.test_resize_embeddings:
            return

        for model_class in self.all_model_classes:
            config = copy.deepcopy(original_config)
            model = model_class(config)
            model.to(torch_device)

            if self.model_tester.is_training is False:
                model.eval()

            model_vocab_size = config.vocab_size
            # Retrieve the embeddings and clone theme
            model_embed = model.resize_token_embeddings(model_vocab_size)
            cloned_embeddings = [emb.weight.clone() for emb in model_embed.emb_layers]
            # Retrieve the cutoffs and copy them
            copied_cutoffs = copy.copy(model_embed.cutoffs)

            test_layers = [x for x in range(config.div_val)]
            for layer in test_layers:
                # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
                model_embed = model.resize_token_embeddings(model_vocab_size + 10, layer)
                self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
                # Check that it actually resizes the embeddings matrix
                self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0] + 10)
                # Check that the cutoffs were modified accordingly
                self.check_cutoffs_and_n_token(
                    copied_cutoffs, layer, model_embed, model, model_class, 10, model_vocab_size
                )

                # Check that the model can still do a forward pass successfully (every parameter should be resized)
                model(**inputs_dict)

                # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
                model_embed = model.resize_token_embeddings(model_vocab_size - 5, layer)
                self.assertEqual(model.config.vocab_size, model_vocab_size - 5)
                # Check that it actually resizes the embeddings matrix
                self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0] - 5)
                # Check that the cutoffs were modified accordingly
                self.check_cutoffs_and_n_token(
                    copied_cutoffs, layer, model_embed, model, model_class, -5, model_vocab_size
                )

                # Check that the model can still do a forward pass successfully (every parameter should be resized)
                # Input ids should be clamped to the maximum size of the vocabulary
                inputs_dict["input_ids"].clamp_(max=model_vocab_size - 5 - 1)
                model(**inputs_dict)

                # Check that adding and removing tokens has not modified the first part of the embedding matrix.
                models_equal = True
                for p1, p2 in zip(cloned_embeddings[layer], model_embed.emb_layers[layer].weight):
                    if p1.data.ne(p2.data).sum() > 0:
                        models_equal = False

                self.assertTrue(models_equal)

                # Reset model embeddings to original size
                model.resize_token_embeddings(model_vocab_size, layer)
                self.assertEqual(model_vocab_size, model.config.vocab_size)
                self.assertEqual(model_embed.emb_layers[layer].weight.shape[0], cloned_embeddings[layer].shape[0])


class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_transfo_xl_wt103(self):
        model = TransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
        model.to(torch_device)
        input_ids = torch.tensor(
            [
                [
                    33,
                    1297,
                    2,
                    1,
                    1009,
                    4,
                    1109,
                    11739,
                    4762,
                    358,
                    5,
                    25,
                    245,
                    22,
                    1706,
                    17,
                    20098,
                    5,
                    3215,
                    21,
                    37,
                    1110,
                    3,
                    13,
                    1041,
                    4,
                    24,
                    603,
                    490,
                    2,
                    71477,
                    20098,
                    104447,
                    2,
                    20961,
                    1,
                    2604,
                    4,
                    1,
                    329,
                    3,
                    6224,
                    831,
                    16002,
                    2,
                    8,
                    603,
                    78967,
                    29546,
                    23,
                    803,
                    20,
                    25,
                    416,
                    5,
                    8,
                    232,
                    4,
                    277,
                    6,
                    1855,
                    4601,
                    3,
                    29546,
                    54,
                    8,
                    3609,
                    5,
                    57211,
                    49,
                    4,
                    1,
                    277,
                    18,
                    8,
                    1755,
                    15691,
                    3,
                    341,
                    25,
                    416,
                    693,
                    42573,
                    71,
                    17,
                    401,
                    94,
                    31,
                    17919,
                    2,
                    29546,
                    7873,
                    18,
                    1,
                    435,
                    23,
                    11011,
                    755,
                    5,
                    5167,
                    3,
                    7983,
                    98,
                    84,
                    2,
                    29546,
                    3267,
                    8,
                    3609,
                    4,
                    1,
                    4865,
                    1075,
                    2,
                    6087,
                    71,
                    6,
                    346,
                    8,
                    5854,
                    3,
                    29546,
                    824,
                    1400,
                    1868,
                    2,
                    19,
                    160,
                    2,
                    311,
                    8,
                    5496,
                    2,
                    20920,
                    17,
                    25,
                    15097,
                    3,
                    24,
                    24,
                    0,
                ]
            ],
            dtype=torch.long,
            device=torch_device,
        )
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
        #  remainder of the story . 1883 Western Siberia ,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,

        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        expected_output_ids = [
            33,
            1297,
            2,
            1,
            1009,
            4,
            1109,
            11739,
            4762,
            358,
            5,
            25,
            245,
            22,
            1706,
            17,
            20098,
            5,
            3215,
            21,
            37,
            1110,
            3,
            13,
            1041,
            4,
            24,
            603,
            490,
            2,
            71477,
            20098,
            104447,
            2,
            20961,
            1,
            2604,
            4,
            1,
            329,
            3,
            6224,
            831,
            16002,
            2,
            8,
            603,
            78967,
            29546,
            23,
            803,
            20,
            25,
            416,
            5,
            8,
            232,
            4,
            277,
            6,
            1855,
            4601,
            3,
            29546,
            54,
            8,
            3609,
            5,
            57211,
            49,
            4,
            1,
            277,
            18,
            8,
            1755,
            15691,
            3,
            341,
            25,
            416,
            693,
            42573,
            71,
            17,
            401,
            94,
            31,
            17919,
            2,
            29546,
            7873,
            18,
            1,
            435,
            23,
            11011,
            755,
            5,
            5167,
            3,
            7983,
            98,
            84,
            2,
            29546,
            3267,
            8,
            3609,
            4,
            1,
            4865,
            1075,
            2,
            6087,
            71,
            6,
            346,
            8,
            5854,
            3,
            29546,
            824,
            1400,
            1868,
            2,
            19,
            160,
            2,
            311,
            8,
            5496,
            2,
            20920,
            17,
            25,
            15097,
            3,
            24,
            24,
            0,
            33,
            1,
            1857,
            2,
            1,
            1009,
            4,
            1109,
            11739,
            4762,
            358,
            5,
            25,
            245,
            28,
            1110,
            3,
            13,
            1041,
            4,
            24,
            603,
            490,
            2,
            71477,
            20098,
            104447,
            2,
            20961,
            1,
            2604,
            4,
            1,
            329,
            3,
            0,
        ]
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (
        #  except for Alexei and Maria ) are discovered. The voice of young son,
        #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
        #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
        #  and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially
        #  slaps him for making such an accusation, Rasputin watches as the man
        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
        #  of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
        # Nicholas II and his family were discovered. The voice of <unk> young son,
        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

        output_ids = model.generate(input_ids, max_length=200, do_sample=False)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)