Switch test files to the standard test_*.py scheme.
This commit is contained in:
Aymeric Augustin
214
tests/test_modeling_transfo_xl.py
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214
tests/test_modeling_transfo_xl.py
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import, division, print_function
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import random
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import unittest
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from transformers import is_torch_available
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import CommonTestCases, ids_tensor
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from .utils import CACHE_DIR, require_torch, slow, torch_device
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if is_torch_available():
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import torch
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from transformers import TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel
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from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
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@require_torch
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class TransfoXLModelTest(CommonTestCases.CommonModelTester):
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all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else ()
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test_pruning = False
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test_torchscript = False
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test_resize_embeddings = False
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class TransfoXLModelTester(object):
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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mem_len=30,
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clamp_len=15,
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is_training=True,
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use_labels=True,
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vocab_size=99,
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cutoffs=[10, 50, 80],
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hidden_size=32,
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d_embed=32,
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num_attention_heads=4,
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d_head=8,
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d_inner=128,
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div_val=2,
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num_hidden_layers=5,
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scope=None,
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seed=1,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.mem_len = mem_len
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self.key_length = seq_length + mem_len
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self.clamp_len = clamp_len
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self.is_training = is_training
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.cutoffs = cutoffs
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self.hidden_size = hidden_size
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self.d_embed = d_embed
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self.num_attention_heads = num_attention_heads
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self.d_head = d_head
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self.d_inner = d_inner
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self.div_val = div_val
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self.num_hidden_layers = num_hidden_layers
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self.scope = scope
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self.seed = seed
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def prepare_config_and_inputs(self):
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input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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lm_labels = None
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if self.use_labels:
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lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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config = TransfoXLConfig(
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vocab_size=self.vocab_size,
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mem_len=self.mem_len,
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clamp_len=self.clamp_len,
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cutoffs=self.cutoffs,
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d_model=self.hidden_size,
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d_embed=self.d_embed,
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n_head=self.num_attention_heads,
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d_head=self.d_head,
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d_inner=self.d_inner,
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div_val=self.div_val,
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n_layer=self.num_hidden_layers,
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)
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return (config, input_ids_1, input_ids_2, lm_labels)
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def set_seed(self):
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random.seed(self.seed)
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torch.manual_seed(self.seed)
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def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels):
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model = TransfoXLModel(config)
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model.to(torch_device)
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model.eval()
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hidden_states_1, mems_1 = model(input_ids_1)
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hidden_states_2, mems_2 = model(input_ids_2, mems_1)
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outputs = {
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"hidden_states_1": hidden_states_1,
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"mems_1": mems_1,
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"hidden_states_2": hidden_states_2,
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"mems_2": mems_2,
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}
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return outputs
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def check_transfo_xl_model_output(self, result):
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self.parent.assertListEqual(
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list(result["hidden_states_1"].size()), [self.batch_size, self.seq_length, self.hidden_size]
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)
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self.parent.assertListEqual(
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list(result["hidden_states_2"].size()), [self.batch_size, self.seq_length, self.hidden_size]
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)
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_1"]),
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[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
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)
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_2"]),
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[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
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)
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def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels):
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model = TransfoXLLMHeadModel(config)
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model.to(torch_device)
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model.eval()
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lm_logits_1, mems_1 = model(input_ids_1)
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loss_1, _, mems_1 = model(input_ids_1, labels=lm_labels)
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lm_logits_2, mems_2 = model(input_ids_2, mems=mems_1)
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loss_2, _, mems_2 = model(input_ids_2, labels=lm_labels, mems=mems_1)
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outputs = {
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"loss_1": loss_1,
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"mems_1": mems_1,
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"lm_logits_1": lm_logits_1,
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"loss_2": loss_2,
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"mems_2": mems_2,
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"lm_logits_2": lm_logits_2,
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}
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return outputs
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def check_transfo_xl_lm_head_output(self, result):
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self.parent.assertListEqual(list(result["loss_1"].size()), [self.batch_size, self.seq_length])
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self.parent.assertListEqual(
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list(result["lm_logits_1"].size()), [self.batch_size, self.seq_length, self.vocab_size]
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)
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_1"]),
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[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
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)
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self.parent.assertListEqual(list(result["loss_2"].size()), [self.batch_size, self.seq_length])
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self.parent.assertListEqual(
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list(result["lm_logits_2"].size()), [self.batch_size, self.seq_length, self.vocab_size]
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)
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_2"]),
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[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
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)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
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inputs_dict = {"input_ids": input_ids_1}
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return config, inputs_dict
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def setUp(self):
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self.model_tester = TransfoXLModelTest.TransfoXLModelTester(self)
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self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_transfo_xl_model(self):
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self.model_tester.set_seed()
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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output_result = self.model_tester.create_transfo_xl_model(*config_and_inputs)
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self.model_tester.check_transfo_xl_model_output(output_result)
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def test_transfo_xl_lm_head(self):
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self.model_tester.set_seed()
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
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self.model_tester.check_transfo_xl_lm_head_output(output_result)
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@slow
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def test_model_from_pretrained(self):
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for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = TransfoXLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
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self.assertIsNotNone(model)
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if __name__ == "__main__":
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unittest.main()
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