# coding=utf-8 # 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. from __future__ import annotations import unittest from transformers import CTRLConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers.models.ctrl.modeling_tf_ctrl import ( TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, TFCTRLForSequenceClassification, TFCTRLLMHeadModel, TFCTRLModel, ) class TFCTRLModelTester(object): def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_token_type_ids = True self.use_input_mask = True self.use_labels = True self.use_mc_token_ids = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.pad_token_id = self.vocab_size - 1 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 = random_attention_mask([self.batch_size, self.seq_length]) 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) mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length) 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 = CTRLConfig( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, dff=self.intermediate_size, # hidden_act=self.hidden_act, # hidden_dropout_prob=self.hidden_dropout_prob, # attention_probs_dropout_prob=self.attention_probs_dropout_prob, n_positions=self.max_position_embeddings, # type_vocab_size=self.type_vocab_size, # initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, ) head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) return ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def create_and_check_ctrl_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFCTRLModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, None, input_mask] # None is the input for 'past' result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_ctrl_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFCTRLLMHeadModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_ctrl_for_sequence_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): config.num_labels = self.num_labels sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) inputs = { "input_ids": input_ids, "token_type_ids": token_type_ids, "labels": sequence_labels, } model = TFCTRLForSequenceClassification(config) result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_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_tf class TFCTRLModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else () all_generative_model_classes = (TFCTRLLMHeadModel,) if is_tf_available() else () pipeline_model_mapping = ( { "feature-extraction": TFCTRLModel, "text-classification": TFCTRLForSequenceClassification, "text-generation": TFCTRLLMHeadModel, "zero-shot": TFCTRLForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests": # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers. # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny # config could not be created. return True return False def setUp(self): self.model_tester = TFCTRLModelTester(self) self.config_tester = ConfigTester(self, config_class=CTRLConfig, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_ctrl_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_ctrl_model(*config_and_inputs) def test_ctrl_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_ctrl_lm_head(*config_and_inputs) def test_ctrl_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_ctrl_for_sequence_classification(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() list_lm_models = [TFCTRLLMHeadModel] list_other_models_with_output_ebd = [TFCTRLForSequenceClassification] for model_class in self.all_model_classes: model = model_class(config) model.build() # may be needed for the get_bias() call below assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in list_lm_models: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) elif model_class in list_other_models_with_output_ebd: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert name is None else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None @slow def test_model_from_pretrained(self): for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFCTRLModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFCTRLModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_ctrl(self): model = TFCTRLLMHeadModel.from_pretrained("Salesforce/ctrl") input_ids = tf.convert_to_tensor([[11859, 0, 1611, 8]], dtype=tf.int32) # Legal the president is expected_output_ids = [ 11859, 0, 1611, 8, 5, 150, 26449, 2, 19, 348, 469, 3, 2595, 48, 20740, 246533, 246533, 19, 30, 5, ] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)