mirror of
https://github.com/huggingface/transformers.git
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252 lines
10 KiB
Python
252 lines
10 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_pretrained_bert import (XLNetConfig, XLNetRunConfig, XLNetModel, XLNetLMHeadModel)
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from pytorch_pretrained_bert.modeling_xlnet import PRETRAINED_MODEL_ARCHIVE_MAP
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class XLNetModelTest(unittest.TestCase):
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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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d_model=32,
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n_head=4,
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d_inner=128,
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n_layer=5,
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max_position_embeddings=10,
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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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seed=1,
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type_vocab_size=2):
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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.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.d_model = d_model
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self.n_head = n_head
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self.d_inner = d_inner
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self.n_layer = n_layer
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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.seed = seed
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self.type_vocab_size = type_vocab_size
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def prepare_config_and_inputs(self):
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input_ids_1 = XLNetModelTest.ids_tensor([self.seq_length, self.batch_size], self.vocab_size)
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input_ids_2 = XLNetModelTest.ids_tensor([self.seq_length, self.batch_size], self.vocab_size)
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segment_ids = XLNetModelTest.ids_tensor([self.seq_length, self.batch_size], self.type_vocab_size)
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# inp_k: int32 Tensor in shape [len, bsz], the input token IDs.
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# seg_id: int32 Tensor in shape [len, bsz], the input segment IDs.
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# input_mask: float32 Tensor in shape [len, bsz], the input mask.
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# 0 for real tokens and 1 for padding.
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# mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
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# from previous batches. The length of the list equals n_layer.
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# If None, no memory is used.
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# perm_mask: float32 Tensor in shape [len, len, bsz].
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# If perm_mask[i, j, k] = 0, i attend to j in batch k;
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# if perm_mask[i, j, k] = 1, i does not attend to j in batch k.
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# If None, each position attends to all the others.
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# target_mapping: float32 Tensor in shape [num_predict, len, bsz].
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# If target_mapping[i, j, k] = 1, the i-th predict in batch k is
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# on the j-th token.
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# Only used during pretraining for partial prediction.
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# Set to None during finetuning.
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# inp_q: float32 Tensor in shape [len, bsz].
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# 1 for tokens with losses and 0 for tokens without losses.
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# Only used during pretraining for two-stream attention.
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# Set to None during finetuning.
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lm_labels = None
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if self.use_labels:
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lm_labels = XLNetModelTest.ids_tensor([self.seq_length, self.batch_size], self.vocab_size)
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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.d_model,
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n_head=self.n_head,
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d_inner=self.d_inner,
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n_layer=self.n_layer,
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untie_r=self.untie_r,
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max_position_embeddings=self.max_position_embeddings)
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run_config = XLNetRunConfig(
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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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config.update(run_config)
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return (config, input_ids_1, input_ids_2, segment_ids, lm_labels)
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def set_seed(self):
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random.seed(self.seed)
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torch.manual_seed(self.seed)
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def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, segment_ids, lm_labels):
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model = XLNetLMHeadModel(config)
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model.eval()
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loss_1, mems_1a = model(input_ids_1, seg_id=segment_ids, target=lm_labels)
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lm_logits_1, mems_1b = model(input_ids_1, seg_id=segment_ids)
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loss_2, mems_2a = model(input_ids_2, seg_id=segment_ids, target=lm_labels, mems=mems_1a)
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lm_logits_2, mems_2b = model(input_ids_2, seg_id=segment_ids, mems=mems_1b)
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outputs = {
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"loss_1": loss_1,
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"mems_1a": mems_1a,
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"lm_logits_1": lm_logits_1,
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"mems_1b": mems_1b,
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"loss_2": loss_2,
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"mems_2a": mems_2a,
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"lm_logits_2": lm_logits_2,
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"mems_2b": mems_2b,
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}
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return outputs
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def check_transfo_xl_lm_head_output(self, result):
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self.parent.assertListEqual(
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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["lm_logits_1"].size()),
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[self.seq_length, self.batch_size, self.vocab_size])
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_1a"]),
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[[self.seq_length, self.batch_size, self.d_model]] * self.n_layer)
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_1b"]),
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[[self.seq_length, self.batch_size, self.d_model]] * self.n_layer)
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self.parent.assertListEqual(
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list(mem[~torch.isnan(mem)].sum() for mem in result["mems_1a"]),
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list(mem[~torch.isnan(mem)].sum() for mem in result["mems_1b"]))
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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["lm_logits_2"].size()),
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[self.seq_length, self.batch_size, self.vocab_size])
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_2a"]),
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[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
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self.parent.assertListEqual(
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list(list(mem.size()) for mem in result["mems_2b"]),
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[[self.mem_len, self.batch_size, self.d_model]] * self.n_layer)
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self.parent.assertListEqual(
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list(mem[~torch.isnan(mem)].sum() for mem in result["mems_2a"]),
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list(mem[~torch.isnan(mem)].sum() for mem in result["mems_2b"]))
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def test_default(self):
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self.run_tester(XLNetModelTest.XLNetModelTester(self))
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def test_config_to_json_string(self):
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config = XLNetConfig(vocab_size_or_config_json_file=96, d_model=37)
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obj = json.loads(config.to_json_string())
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self.assertEqual(obj["n_token"], 96)
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self.assertEqual(obj["d_model"], 37)
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def test_config_to_json_file(self):
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config_first = XLNetConfig(vocab_size_or_config_json_file=96, d_model=37)
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json_file_path = "/tmp/config.json"
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config_first.to_json_file(json_file_path)
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config_second = XLNetConfig.from_json_file(json_file_path)
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os.remove(json_file_path)
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self.assertEqual(config_second.to_dict(), config_first.to_dict())
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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_pretrained_bert_test/"
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for model_name in list(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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def run_tester(self, tester):
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config_and_inputs = tester.prepare_config_and_inputs()
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tester.set_seed()
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output_result = tester.create_transfo_xl_lm_head(*config_and_inputs)
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tester.check_transfo_xl_lm_head_output(output_result)
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@classmethod
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def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
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"""Creates a random int32 tensor of the shape within the vocab size."""
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if rng is None:
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rng = random.Random()
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total_dims = 1
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for dim in shape:
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total_dims *= dim
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values = []
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for _ in range(total_dims):
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values.append(rng.randint(0, vocab_size - 1))
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return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
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@classmethod
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def mask_tensor(cls, shape, vocab_size, rng=None, name=None):
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"""Creates a tensor with padding on the right (0.0 for )."""
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if rng is None:
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rng = random.Random()
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total_dims = 1
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for dim in shape:
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total_dims *= dim
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values = []
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for _ in range(total_dims):
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values.append(rng.randint(0, vocab_size - 1))
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return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
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if __name__ == "__main__":
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unittest.main()
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