Switch test files to the standard test_*.py scheme.
This commit is contained in:
Aymeric Augustin
407
tests/test_modeling_tf_xlnet.py
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407
tests/test_modeling_tf_xlnet.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 XLNetConfig, is_tf_available
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from .test_configuration_common import ConfigTester
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from .test_modeling_tf_common import TFCommonTestCases, ids_tensor
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from .utils import CACHE_DIR, require_tf, slow
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if is_tf_available():
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import tensorflow as tf
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from transformers.modeling_tf_xlnet import (
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TFXLNetModel,
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TFXLNetLMHeadModel,
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TFXLNetForSequenceClassification,
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TFXLNetForTokenClassification,
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TFXLNetForQuestionAnsweringSimple,
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TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
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)
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@require_tf
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class TFXLNetModelTest(TFCommonTestCases.TFCommonModelTester):
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all_model_classes = (
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(
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TFXLNetModel,
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TFXLNetLMHeadModel,
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TFXLNetForSequenceClassification,
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TFXLNetForTokenClassification,
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TFXLNetForQuestionAnsweringSimple,
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)
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if is_tf_available()
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else ()
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)
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test_pruning = False
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class TFXLNetModelTester(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=10,
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clamp_len=-1,
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reuse_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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num_attention_heads=4,
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d_inner=128,
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num_hidden_layers=5,
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type_sequence_label_size=2,
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untie_r=True,
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bi_data=False,
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same_length=False,
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initializer_range=0.05,
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seed=1,
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type_vocab_size=2,
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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_len = seq_length + mem_len
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self.clamp_len = clamp_len
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self.reuse_len = reuse_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.num_attention_heads = num_attention_heads
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self.d_inner = d_inner
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self.num_hidden_layers = num_hidden_layers
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self.bi_data = bi_data
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self.untie_r = untie_r
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self.same_length = same_length
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self.initializer_range = initializer_range
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self.seed = seed
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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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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segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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input_mask = ids_tensor([self.batch_size, self.seq_length], 2, dtype=tf.float32)
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input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
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perm_mask = tf.zeros((self.batch_size, self.seq_length + 1, self.seq_length), dtype=tf.float32)
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perm_mask_last = tf.ones((self.batch_size, self.seq_length + 1, 1), dtype=tf.float32)
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perm_mask = tf.concat([perm_mask, perm_mask_last], axis=-1)
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# perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
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target_mapping = tf.zeros((self.batch_size, 1, self.seq_length), dtype=tf.float32)
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target_mapping_last = tf.ones((self.batch_size, 1, 1), dtype=tf.float32)
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target_mapping = tf.concat([target_mapping, target_mapping_last], axis=-1)
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# target_mapping[:, 0, -1] = 1.0 # predict last token
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sequence_labels = None
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lm_labels = None
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is_impossible_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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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32)
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config = XLNetConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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n_head=self.num_attention_heads,
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d_inner=self.d_inner,
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n_layer=self.num_hidden_layers,
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untie_r=self.untie_r,
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mem_len=self.mem_len,
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clamp_len=self.clamp_len,
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same_length=self.same_length,
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reuse_len=self.reuse_len,
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bi_data=self.bi_data,
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initializer_range=self.initializer_range,
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num_labels=self.type_sequence_label_size,
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)
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return (
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config,
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input_ids_1,
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input_ids_2,
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input_ids_q,
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perm_mask,
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input_mask,
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target_mapping,
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segment_ids,
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lm_labels,
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sequence_labels,
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is_impossible_labels,
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)
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def set_seed(self):
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random.seed(self.seed)
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tf.random.set_seed(self.seed)
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def create_and_check_xlnet_base_model(
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self,
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config,
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input_ids_1,
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input_ids_2,
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input_ids_q,
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perm_mask,
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input_mask,
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target_mapping,
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segment_ids,
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lm_labels,
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sequence_labels,
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is_impossible_labels,
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):
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model = TFXLNetModel(config)
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inputs = {"input_ids": input_ids_1, "input_mask": input_mask, "token_type_ids": segment_ids}
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_, _ = model(inputs)
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inputs = [input_ids_1, input_mask]
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outputs, mems_1 = model(inputs)
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result = {
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"mems_1": [mem.numpy() for mem in mems_1],
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"outputs": outputs.numpy(),
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}
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config.mem_len = 0
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model = TFXLNetModel(config)
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no_mems_outputs = model(inputs)
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self.parent.assertEqual(len(no_mems_outputs), 1)
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self.parent.assertListEqual(
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list(result["outputs"].shape), [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.shape) for mem in result["mems_1"]),
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[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
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)
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def create_and_check_xlnet_lm_head(
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self,
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config,
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input_ids_1,
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input_ids_2,
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input_ids_q,
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perm_mask,
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input_mask,
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target_mapping,
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segment_ids,
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lm_labels,
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sequence_labels,
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is_impossible_labels,
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):
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model = TFXLNetLMHeadModel(config)
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inputs_1 = {"input_ids": input_ids_1, "token_type_ids": segment_ids}
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all_logits_1, mems_1 = model(inputs_1)
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inputs_2 = {"input_ids": input_ids_2, "mems": mems_1, "token_type_ids": segment_ids}
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all_logits_2, mems_2 = model(inputs_2)
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inputs_3 = {"input_ids": input_ids_q, "perm_mask": perm_mask, "target_mapping": target_mapping}
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logits, _ = model(inputs_3)
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result = {
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"mems_1": [mem.numpy() for mem in mems_1],
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"all_logits_1": all_logits_1.numpy(),
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"mems_2": [mem.numpy() for mem in mems_2],
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"all_logits_2": all_logits_2.numpy(),
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}
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self.parent.assertListEqual(
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list(result["all_logits_1"].shape), [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.shape) for mem in result["mems_1"]),
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[[self.seq_length, 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(result["all_logits_2"].shape), [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.shape) 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_and_check_xlnet_qa(
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self,
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config,
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input_ids_1,
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input_ids_2,
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input_ids_q,
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perm_mask,
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input_mask,
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target_mapping,
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segment_ids,
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lm_labels,
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sequence_labels,
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is_impossible_labels,
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):
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model = TFXLNetForQuestionAnsweringSimple(config)
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inputs = {"input_ids": input_ids_1, "attention_mask": input_mask, "token_type_ids": segment_ids}
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start_logits, end_logits, mems = model(inputs)
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result = {
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"start_logits": start_logits.numpy(),
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"end_logits": end_logits.numpy(),
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"mems": [m.numpy() for m in mems],
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}
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self.parent.assertListEqual(list(result["start_logits"].shape), [self.batch_size, self.seq_length])
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self.parent.assertListEqual(list(result["end_logits"].shape), [self.batch_size, self.seq_length])
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self.parent.assertListEqual(
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list(list(mem.shape) for mem in result["mems"]),
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[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
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)
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def create_and_check_xlnet_sequence_classif(
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self,
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config,
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input_ids_1,
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input_ids_2,
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input_ids_q,
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perm_mask,
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input_mask,
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target_mapping,
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segment_ids,
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lm_labels,
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sequence_labels,
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is_impossible_labels,
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):
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model = TFXLNetForSequenceClassification(config)
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logits, mems_1 = model(input_ids_1)
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result = {
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"mems_1": [mem.numpy() for mem in mems_1],
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"logits": logits.numpy(),
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}
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self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.type_sequence_label_size])
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self.parent.assertListEqual(
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list(list(mem.shape) for mem in result["mems_1"]),
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[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers,
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)
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def create_and_check_xlnet_for_token_classification(
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self,
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config,
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input_ids_1,
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input_ids_2,
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input_ids_q,
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perm_mask,
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input_mask,
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target_mapping,
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segment_ids,
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lm_labels,
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sequence_labels,
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is_impossible_labels,
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):
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config.num_labels = input_ids_1.shape[1]
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model = TFXLNetForTokenClassification(config)
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inputs = {
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"input_ids": input_ids_1,
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"attention_mask": input_mask,
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# 'token_type_ids': token_type_ids
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}
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logits, mems_1 = model(inputs)
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result = {
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"mems_1": [mem.numpy() for mem in mems_1],
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"logits": logits.numpy(),
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}
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self.parent.assertListEqual(
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list(result["logits"].shape), [self.batch_size, self.seq_length, config.num_labels]
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)
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self.parent.assertListEqual(
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list(list(mem.shape) for mem in result["mems_1"]),
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[[self.seq_length, 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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(
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config,
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input_ids_1,
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input_ids_2,
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input_ids_q,
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perm_mask,
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input_mask,
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target_mapping,
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segment_ids,
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lm_labels,
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sequence_labels,
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is_impossible_labels,
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) = 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 = TFXLNetModelTest.TFXLNetModelTester(self)
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self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=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_xlnet_base_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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self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
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def test_xlnet_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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self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
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def test_xlnet_sequence_classif(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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self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
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def test_xlnet_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_xlnet_for_token_classification(*config_and_inputs)
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def test_xlnet_qa(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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self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = TFXLNetModel.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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