351 lines
14 KiB
Python
351 lines
14 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch ESM model."""
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import unittest
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from transformers import EsmConfig, is_torch_available
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from transformers.testing_utils import TestCasePlus, require_bitsandbytes, require_torch, slow, torch_device
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
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from transformers.models.esm.modeling_esm import (
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EsmEmbeddings,
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create_position_ids_from_input_ids,
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)
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# copied from tests.test_modeling_roberta
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class EsmModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=False,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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vocab_size=33,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return EsmConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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pad_token_id=1,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = EsmModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_for_masked_lm(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = EsmForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_for_token_classification(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = EsmForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_forward_and_backwards(
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self,
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config,
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input_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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gradient_checkpointing=False,
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):
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model = EsmForMaskedLM(config)
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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model.to(torch_device)
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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result.loss.backward()
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class EsmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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test_mismatched_shapes = False
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all_model_classes = (
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(
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EsmForMaskedLM,
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EsmModel,
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EsmForSequenceClassification,
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EsmForTokenClassification,
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)
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if is_torch_available()
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else ()
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)
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all_generative_model_classes = ()
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pipeline_model_mapping = (
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{
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"feature-extraction": EsmModel,
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"fill-mask": EsmForMaskedLM,
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"text-classification": EsmForSequenceClassification,
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"token-classification": EsmForTokenClassification,
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"zero-shot": EsmForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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test_sequence_classification_problem_types = True
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model_split_percents = [0.5, 0.8, 0.9]
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def setUp(self):
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self.model_tester = EsmModelTester(self)
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self.config_tester = ConfigTester(self, config_class=EsmConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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def test_esm_gradient_checkpointing(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
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@slow
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def test_model_from_pretrained(self):
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model_name = "facebook/esm2_t6_8M_UR50D"
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model = EsmModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_create_position_ids_respects_padding_index(self):
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"""Ensure that the default position ids only assign a sequential . This is a regression
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test for https://github.com/huggingface/transformers/issues/1761
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The position ids should be masked with the embedding object's padding index. Therefore, the
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first available non-padding position index is EsmEmbeddings.padding_idx + 1
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"""
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config = self.model_tester.prepare_config_and_inputs()[0]
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model = EsmEmbeddings(config=config)
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input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
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expected_positions = torch.as_tensor(
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[
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[
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0 + model.padding_idx + 1,
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1 + model.padding_idx + 1,
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2 + model.padding_idx + 1,
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model.padding_idx,
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]
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]
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)
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position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
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self.assertEqual(position_ids.shape, expected_positions.shape)
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self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
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def test_create_position_ids_from_inputs_embeds(self):
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"""Ensure that the default position ids only assign a sequential . This is a regression
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test for https://github.com/huggingface/transformers/issues/1761
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The position ids should be masked with the embedding object's padding index. Therefore, the
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first available non-padding position index is EsmEmbeddings.padding_idx + 1
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"""
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config = self.model_tester.prepare_config_and_inputs()[0]
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embeddings = EsmEmbeddings(config=config)
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inputs_embeds = torch.empty(2, 4, 30)
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expected_single_positions = [
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0 + embeddings.padding_idx + 1,
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1 + embeddings.padding_idx + 1,
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2 + embeddings.padding_idx + 1,
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3 + embeddings.padding_idx + 1,
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]
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expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
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position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
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self.assertEqual(position_ids.shape, expected_positions.shape)
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self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
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@unittest.skip("Esm does not support embedding resizing")
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def test_resize_embeddings_untied(self):
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pass
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@unittest.skip("Esm does not support embedding resizing")
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def test_resize_tokens_embeddings(self):
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pass
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@slow
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@require_torch
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class EsmModelIntegrationTest(TestCasePlus):
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def test_inference_masked_lm(self):
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with torch.no_grad():
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model = EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
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model.eval()
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input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
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output = model(input_ids)[0]
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vocab_size = 33
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expected_shape = torch.Size((1, 6, vocab_size))
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self.assertEqual(output.shape, expected_shape)
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expected_slice = torch.tensor(
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[[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]
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)
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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def test_inference_no_head(self):
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with torch.no_grad():
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model = EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
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model.eval()
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input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
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output = model(input_ids)[0]
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# compare the actual values for a slice.
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expected_slice = torch.tensor(
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[[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]
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)
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self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
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@require_bitsandbytes
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def test_inference_bitsandbytes(self):
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model = EsmForMaskedLM.from_pretrained("facebook/esm2_t36_3B_UR50D", load_in_8bit=True)
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input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
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# Just test if inference works
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with torch.no_grad():
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_ = model(input_ids)[0]
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model = EsmForMaskedLM.from_pretrained("facebook/esm2_t36_3B_UR50D", load_in_4bit=True)
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input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
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# Just test if inference works
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_ = model(input_ids)[0]
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