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.
from __future__ import absolute_import, division, print_function

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 CACHE_DIR, 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_MAP


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

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

    class TransfoXLModelTester(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,
        ):
            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

        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,
            )

            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])
            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])
            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 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)

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