# Copyright 2020 HuggingFace Inc. team. # # 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 FunnelConfig, FunnelTokenizer, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, FunnelBaseModel, FunnelForMaskedLM, FunnelForMultipleChoice, FunnelForPreTraining, FunnelForQuestionAnswering, FunnelForSequenceClassification, FunnelForTokenClassification, FunnelModel, ) class FunnelModelTester: """You can also import this e.g, from .test_modeling_funnel import FunnelModelTester""" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, block_sizes=[1, 1, 2], num_decoder_layers=1, d_model=32, n_head=4, d_head=8, d_inner=37, hidden_act="gelu_new", hidden_dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, max_position_embeddings=512, type_vocab_size=3, initializer_std=0.02, # Set to a smaller value, so we can keep the small error threshold (1e-5) in the test num_labels=3, num_choices=4, scope=None, base=False, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.block_sizes = block_sizes self.num_decoder_layers = num_decoder_layers self.d_model = d_model self.n_head = n_head self.d_head = d_head self.d_inner = d_inner self.hidden_act = hidden_act self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = 2 self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.initializer_std = initializer_std # Used in the tests to check the size of the first attention layer self.num_attention_heads = n_head # Used in the tests to check the size of the first hidden state self.hidden_size = self.d_model # Used in the tests to check the number of output hidden states/attentions self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers) # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with # the last hidden state of the first block (which is the first hidden state of the decoder). if not base: self.expected_num_hidden_layers = self.num_hidden_layers + 2 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) 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) fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1) config = self.get_config() return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ) def get_config(self): return FunnelConfig( vocab_size=self.vocab_size, block_sizes=self.block_sizes, num_decoder_layers=self.num_decoder_layers, d_model=self.d_model, n_head=self.n_head, d_head=self.d_head, d_inner=self.d_inner, hidden_act=self.hidden_act, hidden_dropout=self.hidden_dropout, attention_dropout=self.attention_dropout, activation_dropout=self.activation_dropout, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_std=self.initializer_std, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = FunnelModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model)) model.config.truncate_seq = False result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model)) model.config.separate_cls = False result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model)) def create_and_check_base_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = FunnelBaseModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model)) model.config.truncate_seq = False result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model)) model.config.separate_cls = False result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model)) def create_and_check_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = FunnelForPreTraining(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = FunnelForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = FunnelForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_choices = self.num_choices model = FunnelForMultipleChoice(config=config) model.to(torch_device) 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_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): config.num_labels = self.num_labels model = FunnelForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ): model = FunnelForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, fake_token_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class FunnelModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( FunnelModel, FunnelForMaskedLM, FunnelForPreTraining, FunnelForQuestionAnswering, FunnelForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": (FunnelBaseModel, FunnelModel), "fill-mask": FunnelForMaskedLM, "question-answering": FunnelForQuestionAnswering, "text-classification": FunnelForSequenceClassification, "token-classification": FunnelForTokenClassification, "zero-shot": FunnelForSequenceClassification, } if is_torch_available() else {} ) # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = FunnelModelTester(self) self.config_tester = ConfigTester(self, config_class=FunnelConfig) def test_config(self): self.config_tester.run_common_tests() 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_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*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_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_question_answering(*config_and_inputs) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.fill_(3) for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]: if hasattr(module, param) and getattr(module, param) is not None: weight = getattr(module, param) weight.data.fill_(3) @require_torch class FunnelBaseModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else () ) def setUp(self): self.model_tester = FunnelModelTester(self, base=True) self.config_tester = ConfigTester(self, config_class=FunnelConfig) def test_config(self): self.config_tester.run_common_tests() def test_base_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_base_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_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) # overwrite from test_modeling_common def test_training(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: if model_class.__name__ == "FunnelBaseModel": continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.fill_(3) for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]: if hasattr(module, param) and getattr(module, param) is not None: weight = getattr(module, param) weight.data.fill_(3) @require_torch @require_sentencepiece @require_tokenizers class FunnelModelIntegrationTest(unittest.TestCase): def test_inference_tiny_model(self): batch_size = 13 sequence_length = 7 input_ids = torch.arange(0, batch_size * sequence_length).long().reshape(batch_size, sequence_length) lengths = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1] token_type_ids = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]) model = FunnelModel.from_pretrained("sgugger/funnel-random-tiny") output = model(input_ids, token_type_ids=token_type_ids)[0].abs() expected_output_sum = torch.tensor(2344.8352) expected_output_mean = torch.tensor(0.8052) torch.testing.assert_close(output.sum(), expected_output_sum, rtol=1e-4, atol=1e-4) torch.testing.assert_close(output.mean(), expected_output_mean, rtol=1e-4, atol=1e-4) attention_mask = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]) output = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)[0].abs() expected_output_sum = torch.tensor(2343.8425) expected_output_mean = torch.tensor(0.8049) torch.testing.assert_close(output.sum(), expected_output_sum, rtol=1e-4, atol=1e-4) torch.testing.assert_close(output.mean(), expected_output_mean, rtol=1e-4, atol=1e-4) @slow def test_inference_model(self): tokenizer = FunnelTokenizer.from_pretrained("huggingface/funnel-small") model = FunnelModel.from_pretrained("huggingface/funnel-small") inputs = tokenizer("Hello! I am the Funnel Transformer model.", return_tensors="pt") output = model(**inputs)[0] expected_output_sum = torch.tensor(235.7246) expected_output_mean = torch.tensor(0.0256) torch.testing.assert_close(output.sum(), expected_output_sum, rtol=1e-4, atol=1e-4) torch.testing.assert_close(output.mean(), expected_output_mean, rtol=1e-4, atol=1e-4)