# Copyright 2022 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. """Testing suite for the PyTorch ESM model.""" import tempfile import unittest import pytest from transformers import BitsAndBytesConfig, EsmConfig, is_torch_available from transformers.testing_utils import ( TestCasePlus, is_flaky, require_bitsandbytes, require_flash_attn, require_torch, require_torch_accelerator, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel from transformers.models.esm.modeling_esm import ( EsmEmbeddings, create_position_ids_from_input_ids, ) # copied from tests.test_modeling_roberta class EsmModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=False, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=33, hidden_size=32, 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, position_embedding_type="rotary", ): 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.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings 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.position_embedding_type = position_embedding_type 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]) 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, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return EsmConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, pad_token_id=1, 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, position_embedding_type=self.position_embedding_type, ) def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = EsmModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_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_for_masked_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = EsmForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, 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, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = EsmForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_forward_and_backwards( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, gradient_checkpointing=False, ): model = EsmForMaskedLM(config) if gradient_checkpointing: model.gradient_checkpointing_enable() model.to(torch_device) result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) result.loss.backward() def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class EsmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): test_mismatched_shapes = False all_model_classes = ( ( EsmForMaskedLM, EsmModel, EsmForSequenceClassification, EsmForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": EsmModel, "fill-mask": EsmForMaskedLM, "text-classification": EsmForSequenceClassification, "token-classification": EsmForTokenClassification, "zero-shot": EsmForSequenceClassification, } if is_torch_available() else {} ) test_sequence_classification_problem_types = True model_split_percents = [0.5, 0.8, 0.9] def setUp(self): self.model_tester = EsmModelTester(self) self.config_tester = ConfigTester(self, config_class=EsmConfig, 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_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_esm_gradient_checkpointing(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True) @slow def test_model_from_pretrained(self): model_name = "facebook/esm2_t6_8M_UR50D" model = EsmModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_create_position_ids_respects_padding_index(self): """This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is EsmEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] model = EsmEmbeddings(config=config) input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]]) expected_positions = torch.as_tensor( [ [ 0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx, ] ] ) position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) def test_create_position_ids_from_inputs_embeds(self): """This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is EsmEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] embeddings = EsmEmbeddings(config=config) inputs_embeds = torch.empty(2, 4, 30) expected_single_positions = [ 0 + embeddings.padding_idx + 1, 1 + embeddings.padding_idx + 1, 2 + embeddings.padding_idx + 1, 3 + embeddings.padding_idx + 1, ] expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions]) position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) @unittest.skip(reason="Esm does not support embedding resizing") def test_resize_embeddings_untied(self): pass @unittest.skip(reason="Esm does not support embedding resizing") def test_resize_tokens_embeddings(self): pass @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test @is_flaky() @slow def test_flash_attn_2_equivalence(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn: self.skipTest(reason="Model does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, dtype=torch.float16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, dtype=torch.float16, attn_implementation="eager") model.to(torch_device) dummy_input = inputs_dict[model_class.main_input_name] dummy_input = dummy_input.to(torch_device) outputs = model(dummy_input, output_hidden_states=True) outputs_fa = model_fa(dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] torch.testing.assert_close(logits_fa, logits, atol=1e-2, rtol=1e-3) @slow @require_torch class EsmModelIntegrationTest(TestCasePlus): def test_inference_masked_lm(self): with torch.no_grad(): model = EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D") model.eval() input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] vocab_size = 33 expected_shape = torch.Size((1, 6, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) def test_inference_no_head(self): with torch.no_grad(): model = EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D") model.eval() input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]) output = model(input_ids)[0] # compare the actual values for a slice. expected_slice = torch.tensor( [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) @require_bitsandbytes def test_inference_bitsandbytes(self): model = EsmForMaskedLM.from_pretrained( "facebook/esm2_t36_3B_UR50D", quantization_config=BitsAndBytesConfig(load_in_8bit=True) ) input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]).to(model.device) # Just test if inference works with torch.no_grad(): _ = model(input_ids)[0] model = EsmForMaskedLM.from_pretrained( "facebook/esm2_t36_3B_UR50D", quantization_config=BitsAndBytesConfig(load_in_4bit=True) ) input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]).to(model.device) # Just test if inference works _ = model(input_ids)[0]