mirror of
https://github.com/huggingface/transformers.git
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316 lines
14 KiB
Python
316 lines
14 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import unittest
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import json
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import random
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import shutil
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import pytest
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import torch
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from pytorch_transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering)
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from pytorch_transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
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from .modeling_common_test import ConfigTester, CommonTestCases, ids_tensor
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class XLNetModelTest(CommonTestCases.CommonModelTester):
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all_model_classes=(XLNetModel, XLNetLMHeadModel,
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XLNetForSequenceClassification, XLNetForQuestionAnswering)
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test_pruning = False
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class XLNetModelTester(object):
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def __init__(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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max_position_embeddings=10,
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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.max_position_embeddings = max_position_embeddings
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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).float()
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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(self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float)
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perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token
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target_mapping = torch.zeros(self.batch_size, 1, self.seq_length + 1, dtype=torch.float)
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target_mapping[:, 0, -1] = 1.0 # predict last token
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inp_q = target_mapping[:, 0, :].clone() # 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).float()
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config = XLNetConfig(
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vocab_size_or_config_json_file=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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max_position_embeddings=self.max_position_embeddings,
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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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return (config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
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target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels)
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def set_seed(self):
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random.seed(self.seed)
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torch.manual_seed(self.seed)
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def create_and_check_xlnet_base_model(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
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target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
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model = XLNetModel(config)
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model.eval()
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_, _ = model(input_ids_1, input_mask=input_mask)
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_, _ = model(input_ids_1, attention_mask=input_mask)
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_, _ = model(input_ids_1, token_type_ids=segment_ids)
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outputs, mems_1 = model(input_ids_1)
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result = {
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"mems_1": mems_1,
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"outputs": outputs,
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}
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self.parent.assertListEqual(
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list(result["outputs"].size()),
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[self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_1"]),
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[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
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def create_and_check_xlnet_lm_head(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
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target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
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model = XLNetLMHeadModel(config)
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model.eval()
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loss_1, all_logits_1, mems_1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels)
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loss_2, all_logits_2, mems_2 = model(input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=mems_1)
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logits, _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping, inp_q=inp_q)
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result = {
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"loss_1": loss_1,
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"mems_1": mems_1,
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"all_logits_1": all_logits_1,
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"loss_2": loss_2,
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"mems_2": mems_2,
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"all_logits_2": all_logits_2,
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}
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self.parent.assertListEqual(
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list(result["loss_1"].size()),
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[])
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self.parent.assertListEqual(
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list(result["all_logits_1"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_1"]),
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[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
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self.parent.assertListEqual(
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list(result["loss_2"].size()),
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[])
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self.parent.assertListEqual(
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list(result["all_logits_2"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_2"]),
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[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
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def create_and_check_xlnet_qa(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
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target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
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model = XLNetForQuestionAnswering(config)
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model.eval()
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outputs = model(input_ids_1)
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start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, mems = outputs
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outputs = model(input_ids_1, 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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outputs = model(input_ids_1, 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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total_loss, start_logits, end_logits, cls_logits, mems = outputs
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outputs = model(input_ids_1, start_positions=sequence_labels,
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end_positions=sequence_labels)
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total_loss, start_logits, end_logits, mems = outputs
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result = {
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"loss": total_loss,
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"start_logits": start_logits,
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"end_logits": end_logits,
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"cls_logits": cls_logits,
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"mems": mems,
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}
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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self.parent.assertListEqual(
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list(result["start_logits"].size()),
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[self.batch_size, self.seq_length])
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self.parent.assertListEqual(
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list(result["end_logits"].size()),
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[self.batch_size, self.seq_length])
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self.parent.assertListEqual(
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list(result["cls_logits"].size()),
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[self.batch_size])
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self.parent.assertListEqual(
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list(list(mem.size()) 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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def create_and_check_xlnet_sequence_classif(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
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target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
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model = XLNetForSequenceClassification(config)
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model.eval()
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logits, mems_1 = model(input_ids_1)
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loss, logits, mems_1 = model(input_ids_1, labels=sequence_labels)
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result = {
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"loss": loss,
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"mems_1": mems_1,
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"logits": logits,
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}
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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self.parent.assertListEqual(
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list(result["logits"].size()),
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[self.batch_size, self.type_sequence_label_size])
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_1"]),
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[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
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target_mapping, inp_q, segment_ids, lm_labels,
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sequence_labels, is_impossible_labels) = config_and_inputs
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inputs_dict = {'input_ids': input_ids_1}
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return config, inputs_dict
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def setUp(self):
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self.model_tester = XLNetModelTest.XLNetModelTester(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_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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@pytest.mark.slow
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def test_model_from_pretrained(self):
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cache_dir = "/tmp/pytorch_transformers_test/"
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for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = XLNetModel.from_pretrained(model_name, cache_dir=cache_dir)
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shutil.rmtree(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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