[UnitTest] Add ernie gram unittest (#3059)

* add ernie gram unittest

* update ernie-gram unittest

* update local branch

* update ernie-gram

* update model_name

Co-authored-by: Guo Sheng <[email protected]>

Author: wj-Mcat

paddlenlp/transformers/ernie/tokenizer.py

@@ -624,6 +624,14 @@ def get_special_tokens_mask(self,
                 [0] * len(token_ids_1)) + [1]
         return [1] + ([0] * len(token_ids_0)) + [1]
 
+    def get_vocab(self):
+        vocab = {
+            self.convert_ids_to_tokens(i): i
+            for i in range(self.vocab_size)
+        }
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
 
 class ErnieTinyTokenizer(PretrainedTokenizer):
     r"""
@@ -984,3 +992,11 @@ def get_special_tokens_mask(self,
             return [1] + ([0] * len(token_ids_0)) + [1] + (
                 [0] * len(token_ids_1)) + [1]
         return [1] + ([0] * len(token_ids_0)) + [1]
+
+    def get_vocab(self):
+        vocab = {
+            self.convert_ids_to_tokens(i): i
+            for i in range(self.vocab_size)
+        }
+        vocab.update(self.added_tokens_encoder)
+        return vocab

paddlenlp/transformers/ernie_gram/tokenizer.py

@@ -89,6 +89,10 @@ class ErnieGramTokenizer(ErnieTokenizer):
             "do_lower_case": True
         },
     }
+    max_model_input_sizes = {
+        "ernie-gram-zh": 512,
+        "ernie-gram-zh-finetuned-dureader-robust": 512,
+    }
 
     def __init__(self,
                  vocab_file,
@@ -105,4 +109,5 @@ def __init__(self,
                                                  sep_token=sep_token,
                                                  pad_token=pad_token,
                                                  cls_token=cls_token,
-                                                 mask_token=mask_token)
+                                                 mask_token=mask_token,
+                                                 **kwargs)

tests/transformers/ernie_gram/__init__.py

@@ -0,0 +1,262 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# 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 typing import Optional, Tuple, Dict, Any
+import paddle
+from paddle import Tensor
+
+from dataclasses import dataclass, asdict, fields, Field
+from paddlenlp.transformers import (ErnieGramModel, ErnieGramPretrainedModel,
+                                    ErnieGramForSequenceClassification,
+                                    ErnieGramForTokenClassification,
+                                    ErnieGramForQuestionAnswering)
+
+from ..test_modeling_common import (ids_tensor, floats_tensor,
+                                    random_attention_mask, ModelTesterMixin)
+from ...testing_utils import slow
+
+
+@dataclass
+class ErnieGramTestModelConfig:
+    """ernie-gram model config which keep consist with pretrained_init_configuration sub fields
+    """
+    attention_probs_dropout_prob: float = 0.1
+    emb_size: int = 768
+    hidden_act: str = "gelu"
+    hidden_dropout_prob: float = 0.1
+    hidden_size: int = 768
+    initializer_range: float = 0.02
+    max_position_embeddings: int = 512
+    num_attention_heads: int = 12
+    num_hidden_layers: int = 12
+    type_vocab_size: int = 2
+    vocab_size: int = 1801
+
+    @property
+    def model_kwargs(self) -> dict:
+        """get the model kwargs configuration to init the model"""
+        model_config_fields: Tuple[Field,
+                                   ...] = fields(ErnieGramTestModelConfig)
+        return {
+            field.name: getattr(self, field.name)
+            for field in model_config_fields
+        }
+
+
+@dataclass
+class ErnieGramTestConfig(ErnieGramTestModelConfig):
+    """all of ErnieGram Test configuration
+    
+    """
+    batch_size: int = 2
+    seq_length: int = 7
+
+    is_training: bool = False
+    use_token_type_ids: bool = True
+    use_attention_mask: bool = True
+
+    # used for sequence classification
+    num_classes: int = 3
+
+    test_resize_embeddings: bool = False
+
+
+class ErnieGramModelTester:
+    """Base ErnieGram Model tester which can test:
+    """
+
+    def __init__(self, parent, config: Optional[ErnieGramTestConfig] = None):
+        self.parent = parent
+        self.config: ErnieGramTestConfig = config or ErnieGramTestConfig()
+
+        self.is_training = self.config.is_training
+
+    def prepare_config_and_inputs(
+            self) -> Tuple[Dict[str, Any], Tensor, Tensor, Tensor]:
+        config = self.config
+        input_ids = ids_tensor([config.batch_size, config.seq_length],
+                               config.vocab_size)
+
+        attention_mask = None
+        if config.use_attention_mask:
+            attention_mask = random_attention_mask(
+                [config.batch_size, config.seq_length])
+
+        token_type_ids = None
+        if config.use_token_type_ids:
+            token_type_ids = paddle.zeros_like(input_ids)
+
+        return config.model_kwargs, input_ids, token_type_ids, attention_mask
+
+    def prepare_config_and_inputs_for_common(self):
+        config, input_ids, token_type_ids, attention_mask = self.prepare_config_and_inputs(
+        )
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+    def create_and_check_model(self, config: Dict[str, Any], input_ids: Tensor,
+                               token_type_ids: Tensor, attention_mask: Tensor):
+        model = ErnieGramModel(**config)
+        model.eval()
+
+        result = model(input_ids,
+                       token_type_ids=token_type_ids,
+                       attention_mask=attention_mask)
+        self.parent.assertEqual(result[0].shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.hidden_size
+        ])
+        self.parent.assertEqual(
+            result[1].shape, [self.config.batch_size, self.config.hidden_size])
+
+    def create_and_check_for_sequence_classification(self, config,
+                                                     input_ids: Tensor,
+                                                     token_type_ids: Tensor,
+                                                     attention_mask: Tensor):
+        model = ErnieGramForSequenceClassification(
+            ErnieGramModel(**config), num_classes=self.config.num_classes)
+        model.eval()
+        result = model(input_ids,
+                       token_type_ids=token_type_ids,
+                       attention_mask=attention_mask)
+        self.parent.assertEqual(
+            result.shape, [self.config.batch_size, self.config.num_classes])
+
+    def create_and_check_for_question_answering(self, config, input_ids: Tensor,
+                                                token_type_ids: Tensor,
+                                                attention_mask: Tensor):
+        model = ErnieGramForQuestionAnswering(ErnieGramModel(**config))
+        model.eval()
+        result = model(input_ids,
+                       token_type_ids=token_type_ids,
+                       attention_mask=attention_mask)
+        self.parent.assertEqual(result.shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.num_classes
+        ])
+
+    def create_and_check_for_token_classification(self, config,
+                                                  input_ids: Tensor,
+                                                  token_type_ids: Tensor,
+                                                  attention_mask: Tensor):
+        model = ErnieGramForTokenClassification(
+            ErnieGramModel(**config), num_classes=self.config.num_classes)
+        model.eval()
+        result = model(input_ids,
+                       token_type_ids=token_type_ids,
+                       attention_mask=attention_mask)
+        self.parent.assertEqual(result.shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.num_classes
+        ])
+
+    def get_config(self) -> dict:
+        """get the base model kwargs
+
+        Returns:
+            dict: the values of kwargs
+        """
+        return self.config.model_kwargs
+
+
+class ErnieGramModelTest(ModelTesterMixin, unittest.TestCase):
+    base_model_class = ErnieGramModel
+
+    all_model_classes = (ErnieGramModel, ErnieGramForSequenceClassification,
+                         ErnieGramForTokenClassification,
+                         ErnieGramForQuestionAnswering)
+
+    def setUp(self):
+        self.model_tester = ErnieGramModelTester(self)
+        self.test_resize_embeddings = self.model_tester.config.test_resize_embeddings
+
+    def get_config():
+        pass
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(
+            *config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(
+            *config_and_inputs)
+
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(
+            *config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in list(
+                ErnieGramPretrainedModel.pretrained_init_configuration)[:1]:
+            model = ErnieGramModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+class ErnieGramModelIntegrationTest(unittest.TestCase):
+
+    @slow
+    def test_inference_no_attention(self):
+        model = ErnieGramModel.from_pretrained("ernie-gram-zh")
+        model.eval()
+        input_ids = paddle.to_tensor(
+            [[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
+        with paddle.no_grad():
+            output = model(input_ids)[0]
+        expected_shape = [1, 11, 768]
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = paddle.to_tensor(
+            [[[-0.43569842, -1.50805628, -2.24448967],
+              [-0.12123521, -1.35024536, -1.76512492],
+              [-0.14853711, -1.13618660, -2.87098265]]])
+        self.assertTrue(
+            paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-5))
+
+    @slow
+    def test_inference_with_attention(self):
+        model = ErnieGramModel.from_pretrained(
+            "ernie-gram-zh-finetuned-dureader-robust")
+        model.eval()
+        input_ids = paddle.to_tensor(
+            [[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
+        attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
+        with paddle.no_grad():
+            output = model(input_ids, attention_mask=attention_mask)[0]
+        expected_shape = [1, 11, 768]
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = paddle.to_tensor(
+            [[[0.37543082, -2.94639230, -2.04799986],
+              [0.14168003, -2.02873731, -2.34919119],
+              [0.70280838, -2.40280604, -1.93488157]]])
+        self.assertTrue(
+            paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
+
+
+if __name__ == "__main__":
+    unittest.main()