# coding=utf-8 # Copyright 2023 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 transformers import MegaConfig, is_torch_available from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MegaForCausalLM, MegaForMaskedLM, MegaForMultipleChoice, MegaForQuestionAnswering, MegaForSequenceClassification, MegaForTokenClassification, MegaModel, ) from transformers.models.mega.modeling_mega import MEGA_PRETRAINED_MODEL_ARCHIVE_LIST class MegaModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_positions=1024, bidirectional=False, # needed for decoding, and can't modify common generation tests; test separately by overriding ema_projection_size=16, shared_representation_size=64, use_chunking=False, chunk_size=32, attention_activation="softmax", use_normalized_ffn=True, nffn_hidden_size=24, add_token_type_embeddings=True, type_vocab_size=2, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): 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.add_token_type_embeddings = add_token_type_embeddings self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_positions = max_positions self.bidirectional = bidirectional self.ema_projection_size = ema_projection_size self.shared_representation_size = shared_representation_size self.use_chunking = use_chunking self.chunk_size = chunk_size self.attention_activation = attention_activation self.use_normalized_ffn = use_normalized_ffn self.nffn_hidden_size = nffn_hidden_size self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.num_attention_heads = 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.add_token_type_embeddings: 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 = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return MegaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, intermediate_size=self.intermediate_size, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, # added args add_token_type_embeddings=self.add_token_type_embeddings, max_positions=self.max_positions, bidirectional=self.bidirectional, ema_projection_size=self.ema_projection_size, shared_representation_size=self.shared_representation_size, use_chunking=self.use_chunking, chunk_size=self.chunk_size, attention_activation=self.attention_activation, use_normalized_ffn=self.use_normalized_ffn, nffn_hidden_size=self.nffn_hidden_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config 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 config.bidirectional = False 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 = MegaModel(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.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, 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 = MegaModel(config) model.to(torch_device) model.eval() result = model( 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( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = MegaForCausalLM(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_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.is_decoder = True config.bidirectional = False config.add_cross_attention = True model = MegaForCausalLM(config=config).to(torch_device).eval() # make sure that ids don't start with pad token mask = input_ids.ne(config.pad_token_id).long() input_ids = input_ids * mask # 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 multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # make sure that ids don't start with pad token mask = next_tokens.ne(config.pad_token_id).long() next_tokens = next_tokens * mask next_mask = ids_tensor((self.batch_size, 1), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-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_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] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -1:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_decoder_model_with_chunking( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.use_chunking = True config.output_attentions = True config.attention_activation = "laplace" config.chunk_size = input_ids.size(1) * 2 model = MegaForCausalLM(config).to(torch_device).eval() input_ids = input_ids.repeat(1, 8) # multiply the sequence length by 8 since we repeat the same ids 8 times in input_ids input_mask = random_attention_mask([self.batch_size, self.seq_length * 8]) result = model(input_ids, attention_mask=input_mask) # test if the sequence length of attentions is same provided chunk_size self.parent.assertEqual(result["attentions"][0].shape[-1], config.chunk_size) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegaForMaskedLM(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_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = MegaForTokenClassification(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_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = MegaForMultipleChoice(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_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegaForQuestionAnswering(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)) # extra checks for Mega-specific model functionality def create_and_check_bidirectionality( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.bidirectional = True model = MegaModel(config) model.to(torch_device) model.eval() # no mask result = model(input_ids) # with mask & token types result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) def check_chunking_shorter_sequence( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.use_chunking = True config.chunk_size = input_ids.size(1) + 25 model = MegaModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) def check_chunking_longer_sequence( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.use_chunking = True # we want the chunk size to be < sequence length, and the sequence length to be a multiple of chunk size config.chunk_size = input_ids.size(1) * 2 model = MegaModel(config) model.to(torch_device) model.eval() result = model( input_ids.repeat(1, 8), ) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length * 8, self.hidden_size)) def check_laplace_self_attention( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.attention_activation = "laplace" model = MegaModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) def check_relu2_self_attention( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.attention_activation = "relu2" model = MegaModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) def check_sequence_length_beyond_max_positions( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.max_positions = self.seq_length - 2 model = MegaModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) 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_torch class MegaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( MegaForCausalLM, MegaForMaskedLM, MegaModel, MegaForSequenceClassification, MegaForTokenClassification, MegaForMultipleChoice, MegaForQuestionAnswering, ) if is_torch_available() else () ) all_generative_model_classes = (MegaForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": MegaModel, "fill-mask": MegaForMaskedLM, "question-answering": MegaForQuestionAnswering, "text-classification": MegaForSequenceClassification, "text-generation": MegaForCausalLM, "token-classification": MegaForTokenClassification, "zero-shot": MegaForSequenceClassification, } if is_torch_available() else {} ) fx_compatible = False test_head_masking = False test_pruning = False def setUp(self): self.model_tester = MegaModelTester(self) self.config_tester = ConfigTester(self, config_class=MegaConfig, hidden_size=37) 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_model_as_decoder(self): 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_model_as_decoder_with_default_input_mask(self): # This regression test was failing with PyTorch < 1.3 ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): 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_decoder_model_with_chunking(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_with_chunking(*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_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_bidirectionality(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bidirectionality(*config_and_inputs) def test_for_chunking_shorter_sequence(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_chunking_shorter_sequence(*config_and_inputs) def test_for_chunking_longer_sequence(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_chunking_longer_sequence(*config_and_inputs) def test_for_laplace_attention(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_laplace_self_attention(*config_and_inputs) def test_for_relu2_attention(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_relu2_self_attention(*config_and_inputs) def test_for_sequence_length_beyond_max_positions(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_sequence_length_beyond_max_positions(*config_and_inputs) def test_generate_fp16(self): config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs_for_decoder() # attention_mask = torch.LongTensor(input_ids.ne(1)).to(torch_device) model = MegaForCausalLM(config).eval().to(torch_device) if torch_device == "cuda": model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_sequence_classification_model(self): config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs() config.num_labels = self.model_tester.num_labels sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = MegaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_sequence_classification_model_for_multi_label(self): config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs() config.num_labels = self.model_tester.num_labels config.problem_type = "multi_label_classification" sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = MegaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @slow def test_model_from_pretrained(self): for model_name in MEGA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = MegaModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_cpu_offload(self): super().test_cpu_offload() @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_disk_offload(self): super().test_disk_offload() @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_model_parallelism(self): super().test_model_parallelism() @unittest.skip( reason=( "Calling `self.attention_function` in `MegaMovingAverageGatedAttention.forward` changes the submodules on " "device 1 to device 0 (also changes `requires_grad`). No idea how this could happen for now." ) ) def test_multi_gpu_data_parallel_forward(self): super().test_multi_gpu_data_parallel_forward() @unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.") def test_torchscript_simple(self): super().test_torchscript_simple() @unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.") def test_torchscript_output_hidden_state(self): super().test_torchscript_output_hidden_state() @unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.") def test_torchscript_output_attentions(self): super().test_torchscript_output_attentions() @require_torch class MegaModelIntegrationTest(TestCasePlus): @slow def test_inference_masked_lm(self): model = MegaForMaskedLM.from_pretrained("mnaylor/mega-base-wikitext") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 50265)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[67.8389, 10.1470, -32.7148], [-11.1655, 29.1152, 23.1304], [-3.8015, 66.0397, 29.6733]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @slow def test_inference_no_head(self): model = MegaModel.from_pretrained("mnaylor/mega-base-wikitext") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 128)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. taken from output[:, :3, :3] expected_slice = torch.tensor( [[[1.1767, -0.6349, 2.8494], [-0.5109, -0.7745, 1.9495], [-0.3287, -0.2111, 3.3367]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))