WIP XLM + refactoring

pytorch_pretrained_bert/tests/modeling_test.py → pytorch_pretrained_bert/tests/modeling_bert_test.py

@@ -16,20 +16,15 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
 import unittest
-import json
-import random
 import shutil
 import pytest
 
-import torch
-
 from pytorch_pretrained_bert import (BertConfig, BertModel, BertForMaskedLM,
                                      BertForNextSentencePrediction, BertForPreTraining,
                                      BertForQuestionAnswering, BertForSequenceClassification,
                                      BertForTokenClassification, BertForMultipleChoice)
-from pytorch_pretrained_bert.modeling import PRETRAINED_MODEL_ARCHIVE_MAP
+from pytorch_pretrained_bert.modeling_bert import PRETRAINED_MODEL_ARCHIVE_MAP
 
 from .model_tests_commons import (create_and_check_commons, ConfigTester, ids_tensor)
 

pytorch_pretrained_bert/tests/modeling_xlm_test.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import unittest
+import shutil
+import pytest
+
+from pytorch_pretrained_bert import (XLMConfig, XLMModel, XLMForQuestionAnswering, XLMForSequenceClassification)
+from pytorch_pretrained_bert.modeling_xlm import PRETRAINED_MODEL_ARCHIVE_MAP
+
+from .model_tests_commons import (create_and_check_commons, ConfigTester, ids_tensor)
+
+
+class XLMModelTest(unittest.TestCase):
+    class XLMModelTester(object):
+
+        def __init__(self,
+                     parent,
+                     batch_size=13,
+                     seq_length=7,
+                     is_training=True,
+                     use_input_lengths=True,
+                     use_token_type_ids=True,
+                     use_labels=True,
+                     gelu_activation=True,
+                     sinusoidal_embeddings=False,
+                     causal=False,
+                     asm=False,
+                     n_langs=2,
+                     vocab_size=99,
+                     n_special=0,
+                     hidden_size=32,
+                     num_hidden_layers=5,
+                     num_attention_heads=4,
+                     hidden_dropout_prob=0.1,
+                     attention_probs_dropout_prob=0.1,
+                     max_position_embeddings=512,
+                     type_vocab_size=16,
+                     type_sequence_label_size=2,
+                     initializer_range=0.02,
+                     num_labels=3,
+                     num_choices=4,
+                     summary_type="last",
+                     use_proj=True,
+                     scope=None,
+                     all_model_classes = (XLMModel,),  # , XLMForSequenceClassification, XLMForTokenClassification),
+                    ):
+            self.parent = parent
+            self.batch_size = batch_size
+            self.seq_length = seq_length
+            self.is_training = is_training
+            self.use_input_lengths = use_input_lengths
+            self.use_token_type_ids = use_token_type_ids
+            self.use_labels = use_labels
+            self.gelu_activation = gelu_activation
+            self.sinusoidal_embeddings = sinusoidal_embeddings
+            self.asm = asm
+            self.n_langs = n_langs
+            self.vocab_size = vocab_size
+            self.n_special = n_special
+            self.summary_type = summary_type
+            self.causal = causal
+            self.use_proj = use_proj
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.n_langs = n_langs
+            self.type_sequence_label_size = type_sequence_label_size
+            self.initializer_range = initializer_range
+            self.summary_type = summary_type
+            self.num_labels = num_labels
+            self.num_choices = num_choices
+            self.scope = scope
+            self.all_model_classes = all_model_classes
+
+        def prepare_config_and_inputs(self):
+            input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+            input_lengths = None
+            if self.use_input_lengths:
+                input_lengths = ids_tensor([self.batch_size], vocab_size=self.seq_length-1)
+
+            token_type_ids = None
+            if self.use_token_type_ids:
+                token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
+
+            sequence_labels = None
+            token_labels = None
+            choice_labels = None
+            if self.use_labels:
+                sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+                token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+                choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+            config = XLMConfig(
+                 vocab_size_or_config_json_file=self.vocab_size,
+                 n_special=self.n_special,
+                 emb_dim=self.hidden_size,
+                 n_layers=self.num_hidden_layers,
+                 n_heads=self.num_attention_heads,
+                 dropout=self.hidden_dropout_prob,
+                 attention_dropout=self.attention_probs_dropout_prob,
+                 gelu_activation=self.gelu_activation,
+                 sinusoidal_embeddings=self.sinusoidal_embeddings,
+                 asm=self.asm,
+                 causal=self.causal,
+                 n_langs=self.n_langs,
+                 max_position_embeddings=self.max_position_embeddings,
+                 initializer_range=self.initializer_range,
+                 summary_type=self.summary_type,
+                 use_proj=self.use_proj)
+
+            return config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, choice_labels
+
+        def check_loss_output(self, result):
+            self.parent.assertListEqual(
+                list(result["loss"].size()),
+                [])
+
+        def create_and_check_xlm_model(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, choice_labels):
+            model = XLMModel(config=config)
+            model.eval()
+            outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids)
+            sequence_output = outputs[0]
+            result = {
+                "sequence_output": sequence_output,
+            }
+            self.parent.assertListEqual(
+                list(result["sequence_output"].size()),
+                [self.batch_size, self.seq_length, self.hidden_size])
+
+
+        # def create_and_check_xlm_for_masked_lm(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, choice_labels):
+        #     model = XLMForMaskedLM(config=config)
+        #     model.eval()
+        #     loss, prediction_scores = model(input_ids, token_type_ids, input_lengths, token_labels)
+        #     result = {
+        #         "loss": loss,
+        #         "prediction_scores": prediction_scores,
+        #     }
+        #     self.parent.assertListEqual(
+        #         list(result["prediction_scores"].size()),
+        #         [self.batch_size, self.seq_length, self.vocab_size])
+        #     self.check_loss_output(result)
+
+
+        # def create_and_check_xlm_for_question_answering(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, choice_labels):
+        #     model = XLMForQuestionAnswering(config=config)
+        #     model.eval()
+        #     loss, start_logits, end_logits = model(input_ids, token_type_ids, input_lengths, sequence_labels, sequence_labels)
+        #     result = {
+        #         "loss": loss,
+        #         "start_logits": start_logits,
+        #         "end_logits": end_logits,
+        #     }
+        #     self.parent.assertListEqual(
+        #         list(result["start_logits"].size()),
+        #         [self.batch_size, self.seq_length])
+        #     self.parent.assertListEqual(
+        #         list(result["end_logits"].size()),
+        #         [self.batch_size, self.seq_length])
+        #     self.check_loss_output(result)
+
+
+        # def create_and_check_xlm_for_sequence_classification(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, choice_labels):
+        #     config.num_labels = self.num_labels
+        #     model = XLMForSequenceClassification(config)
+        #     model.eval()
+        #     loss, logits = model(input_ids, token_type_ids, input_lengths, sequence_labels)
+        #     result = {
+        #         "loss": loss,
+        #         "logits": logits,
+        #     }
+        #     self.parent.assertListEqual(
+        #         list(result["logits"].size()),
+        #         [self.batch_size, self.num_labels])
+        #     self.check_loss_output(result)
+
+
+        # def create_and_check_xlm_for_token_classification(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, choice_labels):
+        #     config.num_labels = self.num_labels
+        #     model = XLMForTokenClassification(config=config)
+        #     model.eval()
+        #     loss, logits = model(input_ids, token_type_ids, input_lengths, token_labels)
+        #     result = {
+        #         "loss": loss,
+        #         "logits": logits,
+        #     }
+        #     self.parent.assertListEqual(
+        #         list(result["logits"].size()),
+        #         [self.batch_size, self.seq_length, self.num_labels])
+        #     self.check_loss_output(result)
+
+
+        # def create_and_check_xlm_for_multiple_choice(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, choice_labels):
+        #     config.num_choices = self.num_choices
+        #     model = XLMForMultipleChoice(config=config)
+        #     model.eval()
+        #     multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        #     multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        #     multiple_choice_input_lengths = input_lengths.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        #     loss, logits = model(multiple_choice_inputs_ids,
+        #                  multiple_choice_token_type_ids,
+        #                  multiple_choice_input_lengths,
+        #                  choice_labels)
+        #     result = {
+        #         "loss": loss,
+        #         "logits": logits,
+        #     }
+        #     self.parent.assertListEqual(
+        #         list(result["logits"].size()),
+        #         [self.batch_size, self.num_choices])
+        #     self.check_loss_output(result)
+
+
+        def create_and_check_xlm_commons(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, choice_labels):
+            inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_lengths}
+            create_and_check_commons(self, config, inputs_dict)
+
+    def test_default(self):
+        self.run_tester(XLMModelTest.XLMModelTester(self))
+
+    def test_config(self):
+        config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
+        config_tester.run_common_tests()
+
+    @pytest.mark.slow
+    def test_model_from_pretrained(self):
+        cache_dir = "/tmp/pytorch_pretrained_bert_test/"
+        for model_name in list(PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            model = XLMModel.from_pretrained(model_name, cache_dir=cache_dir)
+            shutil.rmtree(cache_dir)
+            self.assertIsNotNone(model)
+
+    def run_tester(self, tester):
+        config_and_inputs = tester.prepare_config_and_inputs()
+        tester.create_and_check_xlm_model(*config_and_inputs)
+
+        # config_and_inputs = tester.prepare_config_and_inputs()
+        # tester.create_and_check_xlm_for_masked_lm(*config_and_inputs)
+
+        # config_and_inputs = tester.prepare_config_and_inputs()
+        # tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs)
+
+        # config_and_inputs = tester.prepare_config_and_inputs()
+        # tester.create_and_check_xlm_for_question_answering(*config_and_inputs)
+
+        # config_and_inputs = tester.prepare_config_and_inputs()
+        # tester.create_and_check_xlm_for_sequence_classification(*config_and_inputs)
+
+        # config_and_inputs = tester.prepare_config_and_inputs()
+        # tester.create_and_check_xlm_for_token_classification(*config_and_inputs)
+
+        config_and_inputs = tester.prepare_config_and_inputs()
+        tester.create_and_check_xlm_commons(*config_and_inputs)
+
+if __name__ == "__main__":
+    unittest.main()

pytorch_pretrained_bert/tests/tokenization_test.py → pytorch_pretrained_bert/tests/tokenization_bert_test.py

@@ -20,7 +20,7 @@ from io import open
 import shutil
 import pytest
 
-from pytorch_pretrained_bert.tokenization import (BasicTokenizer,
+from pytorch_pretrained_bert.tokenization_bert import (BasicTokenizer,
                                                   BertTokenizer,
                                                   WordpieceTokenizer,
                                                   _is_control, _is_punctuation,

pytorch_pretrained_bert/tests/tokenization_xlm_test.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import os
+import unittest
+import json
+import shutil
+import pytest
+
+from pytorch_pretrained_bert.tokenization_xlm import XLMTokenizer, PRETRAINED_VOCAB_ARCHIVE_MAP
+
+
+class XLMTokenizationTest(unittest.TestCase):
+
+    def test_full_tokenizer(self):
+        """ Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
+        vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
+                 "w</w>", "r</w>", "t</w>",
+                 "lo", "low", "er</w>",
+                 "low</w>", "lowest</w>", "newer</w>", "wider</w>"]
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+        merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
+        with open("/tmp/openai_tokenizer_vocab_test.json", "w") as fp:
+            fp.write(json.dumps(vocab_tokens))
+            vocab_file = fp.name
+        with open("/tmp/openai_tokenizer_merges_test.txt", "w") as fp:
+            fp.write("\n".join(merges))
+            merges_file = fp.name
+
+        tokenizer = XLMTokenizer(vocab_file, merges_file, special_tokens=["<unk>", "<pad>"])
+        os.remove(vocab_file)
+        os.remove(merges_file)
+
+        text = "lower"
+        bpe_tokens = ["low", "er</w>"]
+        tokens = tokenizer.tokenize(text)
+        self.assertListEqual(tokens, bpe_tokens)
+
+        input_tokens = tokens + ["<unk>"]
+        input_bpe_tokens = [14, 15, 20]
+        self.assertListEqual(
+            tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
+
+        vocab_file, merges_file, special_tokens_file = tokenizer.save_vocabulary(vocab_path="/tmp/")
+        tokenizer_2 = XLMTokenizer.from_pretrained("/tmp/")
+        os.remove(vocab_file)
+        os.remove(merges_file)
+        os.remove(special_tokens_file)
+
+        self.assertListEqual(
+            [tokenizer.encoder, tokenizer.decoder, tokenizer.bpe_ranks,
+             tokenizer.special_tokens, tokenizer.special_tokens_decoder],
+            [tokenizer_2.encoder, tokenizer_2.decoder, tokenizer_2.bpe_ranks,
+             tokenizer_2.special_tokens, tokenizer_2.special_tokens_decoder])
+
+    @pytest.mark.slow
+    def test_tokenizer_from_pretrained(self):
+        cache_dir = "/tmp/pytorch_pretrained_bert_test/"
+        for model_name in list(PRETRAINED_VOCAB_ARCHIVE_MAP.keys())[:1]:
+            tokenizer = XLMTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
+            shutil.rmtree(cache_dir)
+            self.assertIsNotNone(tokenizer)
+
+
+if __name__ == '__main__':
+    unittest.main()

pytorch_pretrained_bert/tokenization_openai.py

@@ -26,7 +26,7 @@ from io import open
 from tqdm import tqdm
 
 from .file_utils import cached_path
-from .tokenization import BasicTokenizer
+from .tokenization_bert import BasicTokenizer
 
 logger = logging.getLogger(__name__)
 

pytorch_pretrained_bert/tokenization_xlm.py

@@ -26,7 +26,7 @@ from io import open
 from tqdm import tqdm
 
 from .file_utils import cached_path
-from .tokenization import BasicTokenizer
+from .tokenization_bert import BasicTokenizer
 
 logger = logging.getLogger(__name__)