952 lines
28 KiB
Python
952 lines
28 KiB
Python
# 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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import random
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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if is_torch_available():
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import torch
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from transformers import (
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XLNetConfig,
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XLNetForMultipleChoice,
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XLNetForQuestionAnswering,
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XLNetForQuestionAnsweringSimple,
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XLNetForSequenceClassification,
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XLNetForTokenClassification,
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XLNetLMHeadModel,
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XLNetModel,
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)
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from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_LIST
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class XLNetModelTester:
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def __init__(
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self,
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parent,
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batch_size=14,
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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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bos_token_id=1,
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eos_token_id=2,
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pad_token_id=5,
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num_choices=4,
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):
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self.parent = parent
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self.batch_size = 14
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self.seq_length = 7
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self.mem_len = 10
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# self.key_len = seq_length + mem_len
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self.clamp_len = -1
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self.reuse_len = 15
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self.is_training = True
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self.use_labels = True
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self.vocab_size = 99
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self.cutoffs = [10, 50, 80]
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self.hidden_size = 32
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self.num_attention_heads = 4
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self.d_inner = 128
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self.num_hidden_layers = 5
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self.type_sequence_label_size = 2
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self.untie_r = True
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self.bi_data = False
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self.same_length = False
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self.initializer_range = 0.05
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self.seed = 1
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self.type_vocab_size = 2
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self.bos_token_id = 1
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self.eos_token_id = 2
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self.pad_token_id = 5
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self.num_choices = 4
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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 = random_attention_mask([self.batch_size, self.seq_length])
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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 = torch.zeros(
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self.batch_size,
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self.seq_length + 1,
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self.seq_length + 1,
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dtype=torch.float,
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device=torch_device,
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)
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perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
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target_mapping = torch.zeros(
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self.batch_size,
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1,
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self.seq_length + 1,
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dtype=torch.float,
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device=torch_device,
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)
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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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token_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).float()
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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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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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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eos_token_id=self.eos_token_id,
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return_dict=True,
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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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token_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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torch.manual_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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token_labels,
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):
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model = XLNetModel(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids_1, input_mask=input_mask)
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result = model(input_ids_1, attention_mask=input_mask)
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result = model(input_ids_1, token_type_ids=segment_ids)
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result = model(input_ids_1)
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config.mem_len = 0
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model = XLNetModel(config)
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model.to(torch_device)
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model.eval()
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base_model_output = model(input_ids_1)
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self.parent.assertEqual(len(base_model_output), 2)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertListEqual(
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[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_model_use_cache(
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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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token_labels,
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):
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model = XLNetModel(config=config)
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model.to(torch_device)
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model.eval()
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# first forward pass
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causal_mask = torch.ones(
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input_ids_1.shape[0],
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input_ids_1.shape[1],
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input_ids_1.shape[1],
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dtype=torch.float,
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device=torch_device,
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)
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causal_mask = torch.triu(causal_mask, diagonal=0)
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outputs_cache = model(input_ids_1, use_cache=True, perm_mask=causal_mask)
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outputs_no_cache = model(input_ids_1, use_cache=False, perm_mask=causal_mask)
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outputs_conf = model(input_ids_1)
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self.parent.assertTrue(len(outputs_cache) == len(outputs_conf))
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self.parent.assertTrue(len(outputs_cache) == len(outputs_no_cache) + 1)
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output, mems = outputs_cache.to_tuple()
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# create hypothetical next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
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# append to next input_ids and token_type_ids
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next_input_ids = torch.cat([input_ids_1, next_tokens], dim=-1)
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# causal mask
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causal_mask = torch.ones(
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input_ids_1.shape[0],
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input_ids_1.shape[1] + 1,
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input_ids_1.shape[1] + 1,
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dtype=torch.float,
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device=torch_device,
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)
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causal_mask = torch.triu(causal_mask, diagonal=0)
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single_mask = torch.ones(input_ids_1.shape[0], 1, 1, dtype=torch.float, device=torch_device)
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# second forward pass
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output_from_no_past = model(next_input_ids, perm_mask=causal_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, mems=mems, perm_mask=single_mask)["last_hidden_state"]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def create_and_check_xlnet_base_model_with_att_output(
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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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token_labels,
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):
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model = XLNetModel(config)
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model.to(torch_device)
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model.eval()
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attentions = model(input_ids_1, target_mapping=target_mapping, output_attentions=True)["attentions"]
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self.parent.assertEqual(len(attentions), config.n_layer)
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self.parent.assertIsInstance(attentions[0], tuple)
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self.parent.assertEqual(len(attentions[0]), 2)
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self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape)
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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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token_labels,
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):
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model = XLNetLMHeadModel(config)
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model.to(torch_device)
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model.eval()
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result1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
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result2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=result1.mems)
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_ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping)
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self.parent.assertEqual(result1.loss.shape, ())
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self.parent.assertEqual(result1.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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self.parent.assertListEqual(
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[mem.shape for mem in result1.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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self.parent.assertEqual(result2.loss.shape, ())
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self.parent.assertEqual(result2.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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self.parent.assertListEqual(
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[mem.shape for mem in result2.mems],
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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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token_labels,
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):
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model = XLNetForQuestionAnswering(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids_1)
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result_with_labels = model(
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input_ids_1,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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cls_index=sequence_labels,
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is_impossible=is_impossible_labels,
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p_mask=input_mask,
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)
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result_with_labels = model(
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input_ids_1,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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cls_index=sequence_labels,
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is_impossible=is_impossible_labels,
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)
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total_loss, mems = result_with_labels.to_tuple()
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result_with_labels = model(
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input_ids_1,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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)
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total_loss, mems = result_with_labels.to_tuple()
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self.parent.assertEqual(result_with_labels.loss.shape, ())
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self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
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self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
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self.parent.assertEqual(
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result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
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)
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self.parent.assertEqual(
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result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
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)
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self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
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self.parent.assertListEqual(
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[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_token_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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token_labels,
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):
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model = XLNetForTokenClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids_1)
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result = model(input_ids_1, labels=token_labels)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.type_sequence_label_size))
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self.parent.assertListEqual(
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[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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token_labels,
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):
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model = XLNetForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids_1)
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result = model(input_ids_1, labels=sequence_labels)
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self.parent.assertEqual(result.loss.shape, ())
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
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self.parent.assertListEqual(
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[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 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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token_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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@require_torch
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class XLNetModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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XLNetModel,
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XLNetLMHeadModel,
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XLNetForTokenClassification,
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XLNetForSequenceClassification,
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XLNetForQuestionAnswering,
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XLNetForQuestionAnsweringSimple,
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XLNetForMultipleChoice,
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)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = (
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(XLNetLMHeadModel,) if is_torch_available() else ()
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) # TODO (PVP): Check other models whether language generation is also applicable
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test_pruning = False
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def setUp(self):
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self.model_tester = XLNetModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_xlnet_base_model(self):
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
|
|
|
|
def test_xlnet_base_model_use_cache(self):
|
|
# checking that in auto-regressive mode, :obj:`use_cache` gives the same results
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_xlnet_model_use_cache(*config_and_inputs)
|
|
|
|
def test_xlnet_base_model_with_att_output(self):
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs)
|
|
|
|
def test_xlnet_lm_head(self):
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
|
|
|
|
def test_xlnet_sequence_classif(self):
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
|
|
|
|
def test_xlnet_token_classif(self):
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs)
|
|
|
|
def test_xlnet_qa(self):
|
|
self.model_tester.set_seed()
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
for model_name in XLNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = XLNetModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class XLNetModelLanguageGenerationTest(unittest.TestCase):
|
|
@slow
|
|
def test_lm_generate_xlnet_base_cased(self):
|
|
model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
|
|
model.to(torch_device)
|
|
input_ids = torch.tensor(
|
|
[
|
|
[
|
|
67,
|
|
2840,
|
|
19,
|
|
18,
|
|
1484,
|
|
20,
|
|
965,
|
|
29077,
|
|
8719,
|
|
1273,
|
|
21,
|
|
45,
|
|
273,
|
|
17,
|
|
10,
|
|
15048,
|
|
28,
|
|
27511,
|
|
21,
|
|
4185,
|
|
11,
|
|
41,
|
|
2444,
|
|
9,
|
|
32,
|
|
1025,
|
|
20,
|
|
8719,
|
|
26,
|
|
23,
|
|
673,
|
|
966,
|
|
19,
|
|
29077,
|
|
20643,
|
|
27511,
|
|
20822,
|
|
20643,
|
|
19,
|
|
17,
|
|
6616,
|
|
17511,
|
|
18,
|
|
8978,
|
|
20,
|
|
18,
|
|
777,
|
|
9,
|
|
19233,
|
|
1527,
|
|
17669,
|
|
19,
|
|
24,
|
|
673,
|
|
17,
|
|
28756,
|
|
150,
|
|
12943,
|
|
4354,
|
|
153,
|
|
27,
|
|
442,
|
|
37,
|
|
45,
|
|
668,
|
|
21,
|
|
24,
|
|
256,
|
|
20,
|
|
416,
|
|
22,
|
|
2771,
|
|
4901,
|
|
9,
|
|
12943,
|
|
4354,
|
|
153,
|
|
51,
|
|
24,
|
|
3004,
|
|
21,
|
|
28142,
|
|
23,
|
|
65,
|
|
20,
|
|
18,
|
|
416,
|
|
34,
|
|
24,
|
|
2958,
|
|
22947,
|
|
9,
|
|
1177,
|
|
45,
|
|
668,
|
|
3097,
|
|
13768,
|
|
23,
|
|
103,
|
|
28,
|
|
441,
|
|
148,
|
|
48,
|
|
20522,
|
|
19,
|
|
12943,
|
|
4354,
|
|
153,
|
|
12860,
|
|
34,
|
|
18,
|
|
326,
|
|
27,
|
|
17492,
|
|
684,
|
|
21,
|
|
6709,
|
|
9,
|
|
8585,
|
|
123,
|
|
266,
|
|
19,
|
|
12943,
|
|
4354,
|
|
153,
|
|
6872,
|
|
24,
|
|
3004,
|
|
20,
|
|
18,
|
|
9225,
|
|
2198,
|
|
19,
|
|
12717,
|
|
103,
|
|
22,
|
|
401,
|
|
24,
|
|
6348,
|
|
9,
|
|
12943,
|
|
4354,
|
|
153,
|
|
1068,
|
|
2768,
|
|
2286,
|
|
19,
|
|
33,
|
|
104,
|
|
19,
|
|
176,
|
|
24,
|
|
9313,
|
|
19,
|
|
20086,
|
|
28,
|
|
45,
|
|
10292,
|
|
9,
|
|
4,
|
|
3,
|
|
]
|
|
],
|
|
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. """
|
|
|
|
expected_output_ids = [
|
|
67,
|
|
2840,
|
|
19,
|
|
18,
|
|
1484,
|
|
20,
|
|
965,
|
|
29077,
|
|
8719,
|
|
1273,
|
|
21,
|
|
45,
|
|
273,
|
|
17,
|
|
10,
|
|
15048,
|
|
28,
|
|
27511,
|
|
21,
|
|
4185,
|
|
11,
|
|
41,
|
|
2444,
|
|
9,
|
|
32,
|
|
1025,
|
|
20,
|
|
8719,
|
|
26,
|
|
23,
|
|
673,
|
|
966,
|
|
19,
|
|
29077,
|
|
20643,
|
|
27511,
|
|
20822,
|
|
20643,
|
|
19,
|
|
17,
|
|
6616,
|
|
17511,
|
|
18,
|
|
8978,
|
|
20,
|
|
18,
|
|
777,
|
|
9,
|
|
19233,
|
|
1527,
|
|
17669,
|
|
19,
|
|
24,
|
|
673,
|
|
17,
|
|
28756,
|
|
150,
|
|
12943,
|
|
4354,
|
|
153,
|
|
27,
|
|
442,
|
|
37,
|
|
45,
|
|
668,
|
|
21,
|
|
24,
|
|
256,
|
|
20,
|
|
416,
|
|
22,
|
|
2771,
|
|
4901,
|
|
9,
|
|
12943,
|
|
4354,
|
|
153,
|
|
51,
|
|
24,
|
|
3004,
|
|
21,
|
|
28142,
|
|
23,
|
|
65,
|
|
20,
|
|
18,
|
|
416,
|
|
34,
|
|
24,
|
|
2958,
|
|
22947,
|
|
9,
|
|
1177,
|
|
45,
|
|
668,
|
|
3097,
|
|
13768,
|
|
23,
|
|
103,
|
|
28,
|
|
441,
|
|
148,
|
|
48,
|
|
20522,
|
|
19,
|
|
12943,
|
|
4354,
|
|
153,
|
|
12860,
|
|
34,
|
|
18,
|
|
326,
|
|
27,
|
|
17492,
|
|
684,
|
|
21,
|
|
6709,
|
|
9,
|
|
8585,
|
|
123,
|
|
266,
|
|
19,
|
|
12943,
|
|
4354,
|
|
153,
|
|
6872,
|
|
24,
|
|
3004,
|
|
20,
|
|
18,
|
|
9225,
|
|
2198,
|
|
19,
|
|
12717,
|
|
103,
|
|
22,
|
|
401,
|
|
24,
|
|
6348,
|
|
9,
|
|
12943,
|
|
4354,
|
|
153,
|
|
1068,
|
|
2768,
|
|
2286,
|
|
19,
|
|
33,
|
|
104,
|
|
19,
|
|
176,
|
|
24,
|
|
9313,
|
|
19,
|
|
20086,
|
|
28,
|
|
45,
|
|
10292,
|
|
9,
|
|
4,
|
|
3,
|
|
19,
|
|
12943,
|
|
4354,
|
|
153,
|
|
27,
|
|
442,
|
|
22,
|
|
2771,
|
|
4901,
|
|
9,
|
|
69,
|
|
27,
|
|
442,
|
|
22,
|
|
2771,
|
|
24,
|
|
11335,
|
|
20,
|
|
18,
|
|
9225,
|
|
2198,
|
|
9,
|
|
69,
|
|
27,
|
|
442,
|
|
22,
|
|
2771,
|
|
24,
|
|
11335,
|
|
20,
|
|
18,
|
|
9225,
|
|
2198,
|
|
9,
|
|
69,
|
|
27,
|
|
442,
|
|
22,
|
|
2771,
|
|
]
|
|
# 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.
|
|
# <sep><cls>, Rasputin is asked to perform magic. He is asked to perform a ritual of the Virgin Mary.
|
|
# He is asked to perform a ritual of the Virgin Mary. He is asked to perform
|
|
|
|
output_ids = model.generate(input_ids, max_length=200, do_sample=False)
|
|
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
|