test_modeling_tf_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 random
import unittest

from transformers import TransfoXLConfig, is_tf_available

from .test_configuration_common import ConfigTester
from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from .utils import CACHE_DIR, require_tf, slow


if is_tf_available():
    import tensorflow as tf
    from transformers.modeling_tf_transfo_xl import (
        TFTransfoXLModel,
        TFTransfoXLLMHeadModel,
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
    )


@require_tf
class TFTransfoXLModelTest(TFModelTesterMixin, unittest.TestCase):

    all_model_classes = (TFTransfoXLModel, TFTransfoXLLMHeadModel) if is_tf_available() else ()
    all_generative_model_classes = () if is_tf_available() else ()
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    test_pruning = False
    test_torchscript = False
    test_resize_embeddings = False

    class TFTransfoXLModelTester(object):
        def __init__(
            self,
            parent,
            batch_size=13,
            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_ids=self.eos_token_id,
            )

            return (config, input_ids_1, input_ids_2, lm_labels)

        def set_seed(self):
            random.seed(self.seed)
            tf.random.set_seed(self.seed)

        def create_and_check_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
            model = TFTransfoXLModel(config)

            hidden_states_1, mems_1 = model(input_ids_1)

            inputs = {"input_ids": input_ids_2, "mems": mems_1}

            hidden_states_2, mems_2 = model(inputs)

            result = {
                "hidden_states_1": hidden_states_1.numpy(),
                "mems_1": [mem.numpy() for mem in mems_1],
                "hidden_states_2": hidden_states_2.numpy(),
                "mems_2": [mem.numpy() for mem in mems_2],
            }

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

        def create_and_check_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
            model = TFTransfoXLLMHeadModel(config)

            lm_logits_1, mems_1 = model(input_ids_1)

            inputs = {"input_ids": input_ids_1, "labels": lm_labels}
            _, mems_1 = model(inputs)

            lm_logits_2, mems_2 = model([input_ids_2, mems_1])

            inputs = {"input_ids": input_ids_1, "mems": mems_1, "labels": lm_labels}

            _, mems_2 = model(inputs)

            result = {
                "mems_1": [mem.numpy() for mem in mems_1],
                "lm_logits_1": lm_logits_1.numpy(),
                "mems_2": [mem.numpy() for mem in mems_2],
                "lm_logits_2": lm_logits_2.numpy(),
            }

            self.parent.assertListEqual(
                list(result["lm_logits_1"].shape), [self.batch_size, self.seq_length, self.vocab_size]
            )
            self.parent.assertListEqual(
                list(list(mem.shape) for mem in result["mems_1"]),
                [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
            )

            self.parent.assertListEqual(
                list(result["lm_logits_2"].shape), [self.batch_size, self.seq_length, self.vocab_size]
            )
            self.parent.assertListEqual(
                list(list(mem.shape) 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 setUp(self):
        self.model_tester = TFTransfoXLModelTest.TFTransfoXLModelTester(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()
        self.model_tester.create_and_check_transfo_xl_model(*config_and_inputs)

    def test_transfo_xl_lm_head(self):
        self.model_tester.set_seed()
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*config_and_inputs)

    @slow
    def test_model_from_pretrained(self):
        for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
            model = TFTransfoXLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
            self.assertIsNotNone(model)