wehub-resource-sync
927 lines
25 KiB
Python
927 lines
25 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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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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import paddle
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from parameterized import parameterized_class
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from paddlenlp.transformers import ( # XLNetForCausalLM,
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XLNetConfig,
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XLNetForMultipleChoice,
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XLNetForQuestionAnswering,
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XLNetForSequenceClassification,
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XLNetForTokenClassification,
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XLNetLMHeadModel,
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XLNetModel,
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XLNetPretrainedModel,
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)
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from ...testing_utils import slow
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from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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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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):
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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.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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self.num_classes = 2
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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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token_type_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 = paddle.zeros([self.batch_size, self.seq_length + 1, self.seq_length + 1])
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perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
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target_mapping = paddle.zeros(
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[
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self.batch_size,
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1,
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self.seq_length + 1,
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]
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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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token_labels = None
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choice_labels = None
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if self.parent.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_classes)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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def get_config(self):
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return 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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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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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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num_classes=self.num_classes,
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num_choices=self.num_choices,
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)
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def set_seed(self):
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random.seed(self.seed)
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paddle.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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = XLNetModel(config)
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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=token_type_ids)
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result = model(input_ids_1, return_dict=self.parent.return_dict)
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config["mem_len"] = 0
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model = XLNetModel(config)
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model.eval()
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model(input_ids_1, return_dict=self.parent.return_dict)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
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def create_and_check_use_mems_train(
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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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = XLNetForSequenceClassification(config)
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model.train()
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train_size = input_ids_1.shape[0]
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batch_size = 4
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for i in range(train_size // batch_size + 1):
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input_ids = input_ids_1[i : (i + 1) * batch_size]
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outputs = model(input_ids=input_ids, return_dict=True)
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self.parent.assertIsNone(outputs.get("mems", None))
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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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = XLNetModel(config)
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model.eval()
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outputs = model(
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input_ids_1, target_mapping=target_mapping, output_attentions=True, return_dict=self.parent.return_dict
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)
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if not self.parent.return_dict:
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assert len(outputs) == 2
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if isinstance(outputs, tuple):
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attentions = outputs[1]
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else:
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attentions = outputs.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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = XLNetLMHeadModel(config)
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model.eval()
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result = model(
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input_ids_1, token_type_ids=token_type_ids, labels=token_labels, return_dict=self.parent.return_dict
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)
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# compatibility with old-school code
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if paddle.is_tensor(result):
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result = [result]
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elif token_labels is not None:
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result = result[1:]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size])
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result = model(
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input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping, return_dict=self.parent.return_dict
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)
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if paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, 1, self.vocab_size])
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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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = XLNetForQuestionAnswering(config)
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model.eval()
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results = []
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result = 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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return_dict=self.parent.return_dict,
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)
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results.append(result)
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result_with_mask = 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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input_mask=input_mask,
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return_dict=self.parent.return_dict,
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)
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results.append(result_with_mask)
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for result in results:
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if token_labels is not None:
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result = result[1:]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length])
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length])
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# compatibility with old-school code
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertEqual(len(result), 2)
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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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = XLNetForTokenClassification(config)
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model.eval()
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result = model(input_ids_1, labels=token_labels, return_dict=self.parent.return_dict)
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# compatibility with old-school code
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if paddle.is_tensor(result):
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result = [result]
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elif token_labels is not None:
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result = result[1:]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.num_classes])
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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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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):
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model = XLNetForSequenceClassification(config)
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model.eval()
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result = model(input_ids_1, labels=sequence_labels, return_dict=self.parent.return_dict)
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# compatibility with old-school code
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if paddle.is_tensor(result):
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result = [result]
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elif token_labels is not None:
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result = result[1:]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.type_sequence_label_size])
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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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token_type_ids,
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sequence_labels,
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token_labels,
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choice_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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@parameterized_class(
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("return_dict", "use_labels"),
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[
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[False, False],
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[False, True],
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[True, False],
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[True, True],
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],
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)
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class XLNetModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = XLNetModel
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use_labels = False
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return_dict = False
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all_model_classes = (
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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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XLNetForMultipleChoice,
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)
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def setUp(self):
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self.model_tester = XLNetModelTester(self)
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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_seq_classification_use_mems_train(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_use_mems_train(*config_and_inputs)
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def test_xlnet_base_model_with_att_output(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_with_att_output(*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_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_token_classif(*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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def test_retain_grad_hidden_states_attentions(self):
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# xlnet cannot keep gradients in attentions or hidden states
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return
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# overwrite from test_modeling_common
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def _mock_init_weights(self, module):
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if hasattr(module, "weight") and module.weight is not None:
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module.weight.data.fill_(3)
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if hasattr(module, "bias") and module.bias is not None:
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module.bias.data.fill_(3)
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for param in ["q", "k", "v", "o", "r", "r_r_bias", "r_s_bias", "r_w_bias", "seg_embed", "mask_emb"]:
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if hasattr(module, param) and getattr(module, param) is not None:
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weight = getattr(module, param)
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weight.data.fill_(3)
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def _check_hidden_states_for_generate(
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self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
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):
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self.assertIsInstance(hidden_states, tuple)
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self.assertListEqual(
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[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
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[True] * len(hidden_states),
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)
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self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
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for idx, iter_hidden_states in enumerate(hidden_states):
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# check hidden size
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for i, layer_hidden_states in enumerate(iter_hidden_states):
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# every 2nd tensor is from extra stream
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if i % 2 != 0:
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seq_len = 1
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else:
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# for first item dummy PAD token is appended so need one more
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seq_len = (min_length + 1) if idx == 0 else min_length
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expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
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self.assertEqual(layer_hidden_states.shape, expected_shape)
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def _check_attentions_for_generate(
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|
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
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):
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self.assertIsInstance(attentions, tuple)
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self.assertListEqual(
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[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
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)
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self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
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for idx, attentions_item in enumerate(attentions):
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for iter_attentions in attentions_item:
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tgt_len = min_length
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# for first item dummy PAD token is appended so need one more
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if idx == 0:
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tgt_len += 1
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src_len = min_length + idx + 1
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expected_shape = (
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batch_size * num_beam_groups,
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config.num_attention_heads,
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tgt_len,
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src_len,
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)
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|
# check attn size
|
|
self.assertListEqual(
|
|
[layer_attention.shape for layer_attention in iter_attentions],
|
|
[expected_shape] * len(iter_attentions),
|
|
)
|
|
|
|
@slow
|
|
@unittest.skip("Skip for missing model weight.")
|
|
def test_model_from_pretrained(self):
|
|
for model_name in list(XLNetPretrainedModel.pretrained_init_configuration)[:1]:
|
|
model = XLNetModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
class XLNetModelLanguageGenerationTest(unittest.TestCase):
|
|
@slow
|
|
@unittest.skip("Skip for missing model weight.")
|
|
def test_lm_generate_xlnet_base_cased(self):
|
|
|
|
model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
|
|
# fmt: off
|
|
input_ids = paddle.to_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,
|
|
]], )
|
|
# fmt: on
|
|
# 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. """
|
|
|
|
# fmt: off
|
|
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,
|
|
]
|
|
# fmt: on
|
|
# 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=39, decode_strategy="greedy_search")
|
|
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|