TF port of ESM (#19587)
* Partial TF port for ESM model * Add ESM-TF tests * Add the various imports for TF-ESM * TF weight conversion almost ready * Stop ignoring the decoder weights in PT * Add tests and lots of fixes * fix-copies * Fix imports, add model docs * Add get_vocab() to tokenizer * Fix vocab links for pretrained files * Allow multiple inputs with a sep * Use EOS as SEP token because ESM vocab lacks SEP * Correctly return special tokens mask from ESM tokenizer * make fixup * Stop testing unsupported embedding resizing * Handle TF bias correctly * Skip all models with slow tokenizers in the token classification test * Fixing the batch/unbatcher of pipelines to accomodate the `None` being passed around. * Fixing pipeline bug caused by slow tokenizer being different. * Update src/transformers/models/esm/modeling_tf_esm.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/models/esm/modeling_tf_esm.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update src/transformers/models/esm/modeling_tf_esm.py Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * Update set_input_embeddings and the copyright notices Co-authored-by: Your Name <you@example.com> Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
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Matt
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tests/models/esm/test_modeling_tf_esm.py
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tests/models/esm/test_modeling_tf_esm.py
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# 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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import unittest
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from transformers import EsmConfig, is_tf_available
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from transformers.testing_utils import require_tf, slow
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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if is_tf_available():
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import numpy
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import tensorflow as tf
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from transformers.models.esm.modeling_tf_esm import (
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TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFEsmForMaskedLM,
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TFEsmForSequenceClassification,
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TFEsmForTokenClassification,
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TFEsmModel,
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)
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# copied from tests.test_modeling_tf_roberta
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class TFEsmModelTester:
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def __init__(
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self,
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parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_input_mask = True
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self.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.intermediate_size = 37
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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self.num_labels = 3
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self.num_choices = 4
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self.scope = None
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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 = EsmConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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pad_token_id=1,
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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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return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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def prepare_config_and_inputs_for_decoder(self):
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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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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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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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encoder_hidden_states,
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encoder_attention_mask,
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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 = TFEsmModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs)
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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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def create_and_check_model_as_decoder(
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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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encoder_hidden_states,
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encoder_attention_mask,
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):
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config.add_cross_attention = True
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model = TFEsmModel(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"encoder_hidden_states": encoder_hidden_states,
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"encoder_attention_mask": encoder_attention_mask,
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}
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result = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs, encoder_hidden_states=encoder_hidden_states)
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# Also check the case where encoder outputs are not passed
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result = model(input_ids, attention_mask=input_mask)
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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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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 = TFEsmForMaskedLM(config=config)
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result = model([input_ids, input_mask])
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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 = TFEsmForTokenClassification(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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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_tf
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class TFEsmModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFEsmModel,
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TFEsmForMaskedLM,
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TFEsmForSequenceClassification,
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TFEsmForTokenClassification,
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)
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if is_tf_available()
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else ()
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)
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test_head_masking = False
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test_onnx = False
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def setUp(self):
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self.model_tester = TFEsmModelTester(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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"""Test the base model"""
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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_as_decoder(self):
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"""Test the base model as a decoder (of an encoder-decoder architecture)
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is_deocder=True + cross_attention + pass encoder outputs
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"""
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
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self.model_tester.create_and_check_model_as_decoder(*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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@slow
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def test_model_from_pretrained(self):
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for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFEsmModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@unittest.skip("Protein models do not support embedding resizing.")
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def test_resize_token_embeddings(self):
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pass
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@unittest.skip("Protein models do not support embedding resizing.")
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def test_save_load_after_resize_token_embeddings(self):
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pass
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@require_tf
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class TFEsmModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_masked_lm(self):
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model = TFEsmForMaskedLM.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D")
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input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
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output = model(input_ids)[0]
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expected_shape = [1, 6, 33]
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self.assertEqual(list(output.numpy().shape), expected_shape)
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# compare the actual values for a slice.
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expected_slice = tf.constant(
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[[[15.0963, -6.6414, -1.1346], [-0.2209, -9.9633, 4.2082], [-1.6045, -10.0011, 1.5882]]]
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)
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self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
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@slow
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def test_inference_no_head(self):
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model = TFEsmModel.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D")
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input_ids = tf.constant([[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 = tf.constant(
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[
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[
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[0.144337, 0.541198, 0.32479298],
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[0.30328932, 0.00519154, 0.31089523],
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[0.32273883, -0.24992886, 0.34143737],
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]
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]
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)
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self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
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