# coding=utf-8 # 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 unittest from transformers import EsmConfig, is_torch_available from transformers.testing_utils import TestCasePlus, require_torch, 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.models.esm.modeling_esmfold import EsmForProteinFolding class EsmFoldModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=False, use_input_mask=True, use_token_type_ids=False, use_labels=False, vocab_size=19, 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, ): 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 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): esmfold_config = { "trunk": { "num_blocks": 2, "sequence_state_dim": 64, "pairwise_state_dim": 16, "sequence_head_width": 4, "pairwise_head_width": 4, "position_bins": 4, "chunk_size": 16, "structure_module": { "ipa_dim": 16, "num_angles": 7, "num_blocks": 2, "num_heads_ipa": 4, "pairwise_dim": 16, "resnet_dim": 16, "sequence_dim": 48, }, }, "fp16_esm": False, "lddt_head_hid_dim": 16, } config = EsmConfig( vocab_size=33, 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, is_folding_model=True, esmfold_config=esmfold_config, ) return config def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = EsmForProteinFolding(config=config).float() 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.positions.shape, (2, self.batch_size, self.seq_length, 14, 3)) self.parent.assertEqual(result.angles.shape, (2, self.batch_size, self.seq_length, 7, 2)) 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 EsmFoldModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): test_mismatched_shapes = False all_model_classes = (EsmForProteinFolding,) if is_torch_available() else () all_generative_model_classes = () pipeline_model_mapping = {} if is_torch_available() else {} test_sequence_classification_problem_types = False def setUp(self): self.model_tester = EsmFoldModelTester(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) @unittest.skip("Does not support attention outputs") def test_attention_outputs(self): pass @unittest.skip def test_correct_missing_keys(self): pass @unittest.skip("Esm does not support embedding resizing") def test_resize_embeddings_untied(self): pass @unittest.skip("Esm does not support embedding resizing") def test_resize_tokens_embeddings(self): pass @unittest.skip("ESMFold does not support passing input embeds!") def test_inputs_embeds(self): pass @unittest.skip("ESMFold does not support head pruning.") def test_head_pruning(self): pass @unittest.skip("ESMFold does not support head pruning.") def test_head_pruning_integration(self): pass @unittest.skip("ESMFold does not support head pruning.") def test_head_pruning_save_load_from_config_init(self): pass @unittest.skip("ESMFold does not support head pruning.") def test_head_pruning_save_load_from_pretrained(self): pass @unittest.skip("ESMFold does not support head pruning.") def test_headmasking(self): pass @unittest.skip("ESMFold does not output hidden states in the normal way.") def test_hidden_states_output(self): pass @unittest.skip("ESMfold does not output hidden states in the normal way.") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip("ESMFold only has one output format.") def test_model_outputs_equivalence(self): pass @unittest.skip("This test doesn't work for ESMFold and doesn't test core functionality") def test_save_load_fast_init_from_base(self): pass @unittest.skip("ESMFold does not support input chunking.") def test_feed_forward_chunking(self): pass @unittest.skip("ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments.") def test_initialization(self): pass @unittest.skip("ESMFold doesn't support torchscript compilation.") def test_torchscript_output_attentions(self): pass @unittest.skip("ESMFold doesn't support torchscript compilation.") def test_torchscript_output_hidden_state(self): pass @unittest.skip("ESMFold doesn't support torchscript compilation.") def test_torchscript_simple(self): pass @unittest.skip("ESMFold doesn't support data parallel.") def test_multi_gpu_data_parallel_forward(self): pass @require_torch class EsmModelIntegrationTest(TestCasePlus): @slow def test_inference_protein_folding(self): model = EsmForProteinFolding.from_pretrained("facebook/esmfold_v1").float() model.eval() input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]) position_outputs = model(input_ids)["positions"] expected_slice = torch.tensor([2.5828, 0.7993, -10.9334], dtype=torch.float32) self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0], expected_slice, atol=1e-4))