Flaubert PyTorch tests

tests/test_modeling_flaubert.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.
+
+
+import unittest
+
+from transformers import is_torch_available
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_common import ModelTesterMixin, ids_tensor
+from .utils import CACHE_DIR, require_torch, slow, torch_device
+
+
+if is_torch_available():
+    from transformers import (
+        FlaubertConfig,
+        FlaubertModel,
+        FlaubertWithLMHeadModel,
+        FlaubertForQuestionAnswering,
+        FlaubertForQuestionAnsweringSimple,
+        FlaubertForSequenceClassification,
+    )
+    from transformers.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+
+
+@require_torch
+class FlaubertModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            FlaubertModel,
+            FlaubertWithLMHeadModel,
+            FlaubertForQuestionAnswering,
+            FlaubertForQuestionAnsweringSimple,
+            FlaubertForSequenceClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+
+    class FlaubertModelTester(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,
+        ):
+            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
+
+        def prepare_config_and_inputs(self):
+            input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+            input_mask = ids_tensor([self.batch_size, self.seq_length], 2).float()
+
+            input_lengths = None
+            if self.use_input_lengths:
+                input_lengths = (
+                        ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
+                )  # small variation of seq_length
+
+            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
+            is_impossible_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)
+                is_impossible_labels = ids_tensor([self.batch_size], 2).float()
+
+            config = FlaubertConfig(
+                vocab_size=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,
+                is_impossible_labels,
+                input_mask,
+            )
+
+        def check_loss_output(self, result):
+            self.parent.assertListEqual(list(result["loss"].size()), [])
+
+        def create_and_check_flaubert_model(
+                self,
+                config,
+                input_ids,
+                token_type_ids,
+                input_lengths,
+                sequence_labels,
+                token_labels,
+                is_impossible_labels,
+                input_mask,
+        ):
+            model = FlaubertModel(config=config)
+            model.to(torch_device)
+            model.eval()
+            outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids)
+            outputs = model(input_ids, langs=token_type_ids)
+            outputs = model(input_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_flaubert_lm_head(
+                self,
+                config,
+                input_ids,
+                token_type_ids,
+                input_lengths,
+                sequence_labels,
+                token_labels,
+                is_impossible_labels,
+                input_mask,
+        ):
+            model = FlaubertWithLMHeadModel(config)
+            model.to(torch_device)
+            model.eval()
+
+            loss, logits = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
+
+            result = {
+                "loss": loss,
+                "logits": logits,
+            }
+
+            self.parent.assertListEqual(list(result["loss"].size()), [])
+            self.parent.assertListEqual(
+                list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size]
+            )
+
+        def create_and_check_flaubert_simple_qa(
+                self,
+                config,
+                input_ids,
+                token_type_ids,
+                input_lengths,
+                sequence_labels,
+                token_labels,
+                is_impossible_labels,
+                input_mask,
+        ):
+            model = FlaubertForQuestionAnsweringSimple(config)
+            model.to(torch_device)
+            model.eval()
+
+            outputs = model(input_ids)
+
+            outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
+            loss, start_logits, end_logits = outputs
+
+            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_flaubert_qa(
+                self,
+                config,
+                input_ids,
+                token_type_ids,
+                input_lengths,
+                sequence_labels,
+                token_labels,
+                is_impossible_labels,
+                input_mask,
+        ):
+            model = FlaubertForQuestionAnswering(config)
+            model.to(torch_device)
+            model.eval()
+
+            outputs = model(input_ids)
+            start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = outputs
+
+            outputs = model(
+                input_ids,
+                start_positions=sequence_labels,
+                end_positions=sequence_labels,
+                cls_index=sequence_labels,
+                is_impossible=is_impossible_labels,
+                p_mask=input_mask,
+            )
+
+            outputs = model(
+                input_ids,
+                start_positions=sequence_labels,
+                end_positions=sequence_labels,
+                cls_index=sequence_labels,
+                is_impossible=is_impossible_labels,
+            )
+
+            (total_loss,) = outputs
+
+            outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
+
+            (total_loss,) = outputs
+
+            result = {
+                "loss": total_loss,
+                "start_top_log_probs": start_top_log_probs,
+                "start_top_index": start_top_index,
+                "end_top_log_probs": end_top_log_probs,
+                "end_top_index": end_top_index,
+                "cls_logits": cls_logits,
+            }
+
+            self.parent.assertListEqual(list(result["loss"].size()), [])
+            self.parent.assertListEqual(
+                list(result["start_top_log_probs"].size()), [self.batch_size, model.config.start_n_top]
+            )
+            self.parent.assertListEqual(
+                list(result["start_top_index"].size()), [self.batch_size, model.config.start_n_top]
+            )
+            self.parent.assertListEqual(
+                list(result["end_top_log_probs"].size()),
+                [self.batch_size, model.config.start_n_top * model.config.end_n_top],
+            )
+            self.parent.assertListEqual(
+                list(result["end_top_index"].size()),
+                [self.batch_size, model.config.start_n_top * model.config.end_n_top],
+            )
+            self.parent.assertListEqual(list(result["cls_logits"].size()), [self.batch_size])
+
+        def create_and_check_flaubert_sequence_classif(
+                self,
+                config,
+                input_ids,
+                token_type_ids,
+                input_lengths,
+                sequence_labels,
+                token_labels,
+                is_impossible_labels,
+                input_mask,
+        ):
+            model = FlaubertForSequenceClassification(config)
+            model.to(torch_device)
+            model.eval()
+
+            (logits,) = model(input_ids)
+            loss, logits = model(input_ids, labels=sequence_labels)
+
+            result = {
+                "loss": loss,
+                "logits": logits,
+            }
+
+            self.parent.assertListEqual(list(result["loss"].size()), [])
+            self.parent.assertListEqual(
+                list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size]
+            )
+
+        def prepare_config_and_inputs_for_common(self):
+            config_and_inputs = self.prepare_config_and_inputs()
+            (
+                config,
+                input_ids,
+                token_type_ids,
+                input_lengths,
+                sequence_labels,
+                token_labels,
+                is_impossible_labels,
+                input_mask,
+            ) = config_and_inputs
+            inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
+            return config, inputs_dict
+
+    def setUp(self):
+        self.model_tester = FlaubertModelTest.FlaubertModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_flaubert_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_flaubert_model(*config_and_inputs)
+
+    def test_flaubert_lm_head(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs)
+
+    def test_flaubert_simple_qa(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_flaubert_simple_qa(*config_and_inputs)
+
+    def test_flaubert_qa(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_flaubert_qa(*config_and_inputs)
+
+    def test_flaubert_sequence_classif(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_flaubert_sequence_classif(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in list(Flaubert_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+            model = FlaubertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
+            self.assertIsNotNone(model)