Okay.
Let see if I can explain myself better with code.
This is part of my training notebook (maybe are left some imports)
As you appreciate, I set the constraint max_length=256, so longer sentences are truncated up to this length.
# download tokenizer
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)

# tokenizer helper function
def tokenize(batch):
    return tokenizer(batch['text'], padding='max_length', truncation=True, max_length=256)

# tokenize dataset
train_dataset = train_dataset.map(tokenize, batched=True)
test_dataset = test_dataset.map(tokenize, batched=True)

# set format for pytorch
# train_dataset =  train_dataset.rename_column("label", "labels")
train_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels'])
# test_dataset = test_dataset.rename_column("label", "labels")
test_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels'])

import botocore
from datasets.filesystems import S3FileSystem

s3 = S3FileSystem()  

# save train_dataset to s3
training_input_path = f's3://{sess.default_bucket()}/{s3_prefix}/train'
train_dataset.save_to_disk(training_input_path,fs=s3)

# save test_dataset to s3
test_input_path = f's3://{sess.default_bucket()}/{s3_prefix}/test'
test_dataset.save_to_disk(test_input_path,fs=s3)

from sagemaker.huggingface import HuggingFace

# hyperparameters, which are passed into the training job
hyperparameters={
    'epochs': 3,
    'train_batch_size': 32,
    'model_name': checkpoint
                 }

huggingface_estimator = HuggingFace(entry_point='train.py',
                            source_dir='./scripts',
                            instance_type='ml.p3.2xlarge',
                            instance_count=1,
                            role=role,
                            transformers_version='4.6',
                            pytorch_version='1.7',
                            py_version='py36',
                            hyperparameters = hyperparameters)

huggingface_estimator.fit({'train': training_input_path, 'test': test_input_path})

Now we go to predict. We will use to predict a sentence longer than 512 tokens (this is longer than my max_length, but also longer than the BERT max_length)
predictor = huggingface_estimator.deploy(1,"ml.g4dn.xlarge")

long_sentence = "......" # longer than 512 tokens
sentiment_input= {
   "inputs": long_sentence}
predictor.predict(sentiment_input)

This throw this error:

The deployment step is not using my tokenizer, since it were so, all sentences would long 256…but it isn’t. So the question is: How can I force to use my personal tokenizer at inference time?