[Unittest]add roformerv2 unittest

paddlenlp/transformers/roformerv2/modeling.py

@@ -520,6 +520,12 @@ def forward(self,
 
         return outputs
 
+    def get_input_embeddings(self) -> nn.Embedding:
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, embedding: nn.Embedding):
+        self.embeddings.word_embeddings = embedding
+
 
 class RoFormerv2ForQuestionAnswering(RoFormerv2PretrainedModel):
     """

paddlenlp/transformers/roformerv2/tokenizer.py

@@ -100,6 +100,13 @@ class RoFormerv2Tokenizer(PretrainedTokenizer):
             "do_lower_case": True
         },
     }
+
+    # TODO(wj-Mcat): to be confirmed
+    max_model_input_sizes = {
+        "roformer_v2_chinese_char_small": 1024,
+        "roformer_v2_chinese_char_base": 1024,
+        "roformer_v2_chinese_char_large": 1024,
+    }
     padding_side = "right"
     max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 

tests/transformers/roformerv2/test_modeling.py

@@ -0,0 +1,348 @@
+# 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
+from dataclasses import dataclass, fields, Field
+
+import paddle
+from paddlenlp.transformers import (
+    RoFormerv2Model,
+    RoFormerv2ForMaskedLM,
+    RoFormerv2PretrainedModel,
+    RoFormerv2ForSequenceClassification,
+    RoFormerv2ForTokenClassification,
+    RoFormerv2ForQuestionAnswering,
+    RoFormerv2ForMultipleChoice,
+)
+
+from ..test_modeling_common import ids_tensor, floats_tensor, random_attention_mask, ModelTesterMixin
+from ...testing_utils import slow
+
+
+@dataclass
+class RoFormerv2ModelTestModelConfig:
+    """RoFormerv2Model model config which keep consist with pretrained_init_configuration sub fields
+    """
+    vocab_size: int = 200
+    hidden_size: int = 36
+    num_hidden_layers: int = 6
+    num_attention_heads: int = 6
+    intermediate_size: int = 20
+    hidden_act: str = "relu"
+    hidden_dropout_prob: float = 0.1
+    attention_probs_dropout_prob: float = 0.1
+    max_position_embeddings: int = 20
+    type_vocab_size: int = 2
+    pad_token_id: int = 0
+    rotary_value: bool = False
+    use_bias: bool = False
+
+    @property
+    def model_kwargs(self) -> dict:
+        """get the model kwargs configuration to init the model"""
+        model_config_fields: Tuple[Field,
+                                   ...] = fields(RoFormerv2ModelTestModelConfig)
+        return {
+            field.name: getattr(self, field.name)
+            for field in model_config_fields
+        }
+
+
+@dataclass
+class RoFormerv2ModelTestConfig(RoFormerv2ModelTestModelConfig):
+    """train config under unittest code"""
+    batch_size: int = 2
+    seq_length: int = 7
+    is_training: bool = False
+    use_input_mask: bool = False
+    use_token_type_ids: bool = True
+
+    # used for sequence classification
+    num_classes: int = 3
+    num_choices: int = 3
+
+
+class RoFormerv2ModelTester:
+
+    def __init__(
+        self,
+        parent,
+        config: Optional[RoFormerv2ModelTestConfig] = None,
+    ):
+        self.parent = parent
+        self.config: RoFormerv2ModelTestConfig = config or RoFormerv2ModelTestConfig(
+        )
+
+        self.is_training = self.config.is_training
+        self.num_classes = self.config.num_classes
+        self.num_choices = self.config.num_choices
+
+    def prepare_config_and_inputs(self):
+        config = self.config
+        input_ids = ids_tensor([config.batch_size, config.seq_length],
+                               config.vocab_size)
+
+        input_mask = None
+        if self.config.use_input_mask:
+            input_mask = random_attention_mask(
+                [config.batch_size, config.seq_length])
+
+        token_type_ids = None
+        if self.config.use_token_type_ids:
+            token_type_ids = ids_tensor([config.batch_size, config.seq_length],
+                                        config.type_vocab_size)
+
+        config = self.get_config()
+        return config, input_ids, token_type_ids, input_mask
+
+    def get_config(self) -> dict:
+        return self.config.model_kwargs
+
+    def create_and_check_model(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2Model(**config)
+        model.eval()
+        result = model(input_ids,
+                       attention_mask=input_mask,
+                       token_type_ids=token_type_ids,
+                       output_hidden_states=True)
+        result = model(input_ids,
+                       token_type_ids=token_type_ids,
+                       output_hidden_states=True)
+        result = model(input_ids, output_hidden_states=True)
+        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.seq_length,
+            self.config.hidden_size
+        ])
+
+    def create_and_check_for_multiple_choice(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2ForMultipleChoice(RoFormerv2Model(**config),
+                                            num_choices=self.config.num_choices)
+        model.eval()
+        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(
+            [-1, self.config.num_choices, -1])
+
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids.unsqueeze(1).expand(
+                [-1, self.config.num_choices, -1])
+
+        if input_mask is not None:
+            input_mask = input_mask.unsqueeze(1).expand(
+                [-1, self.config.num_choices, -1])
+
+        result = model(
+            multiple_choice_inputs_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+        )
+        self.parent.assertEqual(
+            result.shape, [self.config.batch_size, self.config.num_choices])
+
+    def create_and_check_for_masked_lm(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2ForMaskedLM(RoFormerv2Model(**config))
+        model.eval()
+        result = model(input_ids,
+                       attention_mask=input_mask,
+                       token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.vocab_size
+        ])
+
+    def create_and_check_for_sequence_classification(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2ForSequenceClassification(
+            RoFormerv2Model(**config), num_classes=self.config.num_classes)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+        )
+        self.parent.assertEqual(
+            result.shape, [self.config.batch_size, self.config.num_classes])
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+        ) = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+            "attention_mask": input_mask
+        }
+        return config, inputs_dict
+
+    def create_and_check_for_question_answering(self, config, input_ids,
+                                                token_type_ids, input_mask):
+        model = RoFormerv2ForQuestionAnswering(RoFormerv2Model(**config))
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+        )
+        self.parent.assertEqual(
+            result[0].shape, [self.config.batch_size, self.config.seq_length])
+        self.parent.assertEqual(
+            result[1].shape, [self.config.batch_size, self.config.seq_length])
+
+    def create_and_check_for_token_classification(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+    ):
+        model = RoFormerv2ForTokenClassification(RoFormerv2Model(**config),
+                                                 num_classes=self.num_classes)
+        model.eval()
+        result = model(input_ids,
+                       attention_mask=input_mask,
+                       token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.shape, [
+            self.config.batch_size, self.config.seq_length,
+            self.config.num_classes
+        ])
+
+
+class RoFormerv2ModelTest(ModelTesterMixin, unittest.TestCase):
+    base_model_class = RoFormerv2Model
+
+    all_model_classes = (
+        RoFormerv2ForMaskedLM,
+        RoFormerv2ForSequenceClassification,
+        RoFormerv2ForTokenClassification,
+        RoFormerv2ForQuestionAnswering,
+        RoFormerv2ForMultipleChoice,
+    )
+
+    def setUp(self):
+        self.model_tester = RoFormerv2ModelTester(self)
+
+    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_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_multiple_choice(
+            *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_question_answering(
+            *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)
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in list(
+                RoFormerv2PretrainedModel.pretrained_init_configuration)[:1]:
+            model = RoFormerv2Model.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+class RoFormerv2ModelIntegrationTest(unittest.TestCase):
+
+    @slow
+    def test_inference_no_attention(self):
+        model = RoFormerv2Model.from_pretrained(
+            "roformer_v2_chinese_char_small")
+        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, output_hidden_states=True)[0]
+        expected_shape = [1, 11, 384]
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = paddle.to_tensor(
+            [[[0.75068903, 0.13977423, 0.07971212],
+              [0.08614583, 0.21606587, -1.08551681],
+              [0.98021960, -0.85751861, -1.42552316]]])
+
+        self.assertTrue(
+            paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
+
+    @slow
+    def test_inference_with_attention(self):
+        model = RoFormerv2Model.from_pretrained(
+            "roformer_v2_chinese_char_small")
+        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,
+                           output_hidden_states=True)[0]
+        expected_shape = [1, 11, 384]
+        self.assertEqual(output.shape, expected_shape)
+
+        expected_slice = paddle.to_tensor(
+            [[[0.75068903, 0.13977423, 0.07971212],
+              [0.08614583, 0.21606587, -1.08551681],
+              [0.98021960, -0.85751861, -1.42552316]]])
+        self.assertTrue(
+            paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
+
+
+if __name__ == "__main__":
+    unittest.main()