# Copyright 2021 The HuggingFace Inc. 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 RemBertConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFRemBertForCausalLM, TFRemBertForMaskedLM, TFRemBertForMultipleChoice, TFRemBertForQuestionAnswering, TFRemBertForSequenceClassification, TFRemBertForTokenClassification, TFRemBertModel, ) class TFRemBertModelTester: 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, hidden_size=32, input_embedding_size=18, output_embedding_size=43, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", 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, scope=None, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.input_embedding_size = input_embedding_size self.output_embedding_size = output_embedding_size 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 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) 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 = RemBertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, input_embedding_size=self.input_embedding_size, output_embedding_size=self.output_embedding_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, return_dict=True, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFRemBertModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} inputs = [input_ids, input_mask] 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_causal_lm_base_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TFRemBertModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] 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_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFRemBertModel(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) # Also check the case where encoder outputs are not passed result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TFRemBertForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } prediction_scores = model(inputs)["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_causal_lm_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFRemBertForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask, } result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) prediction_scores = result["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_causal_lm_model_past( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFRemBertForCausalLM(config=config) # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_model_past_with_attn_mask( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFRemBertForCausalLM(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass outputs = model(input_ids, attention_mask=attn_mask, use_cache=True) # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) past_key_values = outputs.past_key_values # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat( [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)], axis=1, ) output_from_no_past = model( next_input_ids, attention_mask=attn_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-6) def create_and_check_causal_lm_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFRemBertForCausalLM(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = TFRemBertForCausalLM(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] encoder_hidden_states = encoder_hidden_states[:1, :, :] encoder_attention_mask = encoder_attention_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFRemBertForMaskedLM(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_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFRemBertForSequenceClassification(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.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = TFRemBertForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) 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 ): config.num_labels = self.num_labels model = TFRemBertForTokenClassification(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.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFRemBertForQuestionAnswering(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) 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, ) = 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 TFRemBertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFRemBertModel, TFRemBertForCausalLM, TFRemBertForMaskedLM, TFRemBertForQuestionAnswering, TFRemBertForSequenceClassification, TFRemBertForTokenClassification, TFRemBertForMultipleChoice, ) if is_tf_available() else () ) pipeline_model_mapping = ( { "feature-extraction": TFRemBertModel, "fill-mask": TFRemBertForMaskedLM, "question-answering": TFRemBertForQuestionAnswering, "text-classification": TFRemBertForSequenceClassification, "text-generation": TFRemBertForCausalLM, "token-classification": TFRemBertForTokenClassification, "zero-shot": TFRemBertForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFRemBertModelTester(self) self.config_tester = ConfigTester(self, config_class=RemBertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): """Test the base model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_causal_lm_base_model(self): """Test the base model of the causal LM model is_decoder=True, no cross_attention, no encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs) def test_model_as_decoder(self): """Test the base model as a decoder (of an encoder-decoder architecture) is_decoder=True + cross_attention + pass encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*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_causal_lm(self): """Test the causal LM model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model(*config_and_inputs) def test_causal_lm_model_as_decoder(self): """Test the causal LM model as a decoder""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs) def test_causal_lm_model_past(self): """Test causal LM model with `past_key_values`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs) def test_causal_lm_model_past_with_attn_mask(self): """Test the causal LM model with `past_key_values` and `attention_mask`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs) def test_causal_lm_model_past_with_large_inputs(self): """Test the causal LM model with `past_key_values` and a longer decoder sequence length""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): """Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*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): model = TFRemBertModel.from_pretrained("google/rembert") self.assertIsNotNone(model) @require_tf class TFRemBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_model(self): model = TFRemBertModel.from_pretrained("google/rembert") input_ids = tf.constant([[312, 56498, 313, 2125, 313]]) segment_ids = tf.constant([[0, 0, 0, 1, 1]]) output = model(input_ids, token_type_ids=segment_ids, output_hidden_states=True) hidden_size = 1152 expected_shape = [1, 5, hidden_size] self.assertEqual(output["last_hidden_state"].shape, expected_shape) expected_implementation = tf.constant( [ [ [0.0754, -0.2022, 0.1904], [-0.3354, -0.3692, -0.4791], [-0.2314, -0.6729, -0.0749], [-0.0396, -0.3105, -0.4234], [-0.1571, -0.0525, 0.5353], ] ] ) tf.debugging.assert_near(output["last_hidden_state"][:, :, :3], expected_implementation, atol=1e-4) # Running on the original tf implementation gives slightly different results here. # Not clear why this variations is present # TODO: Find reason for discrepancy # expected_original_implementation = [[ # [0.07630594074726105, -0.20146065950393677, 0.19107051193714142], # [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135], # [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716], # [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626], # [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198] # ]]