 Here is the Python code based on your instructions. I've used the Hugging Face Transformers library for the Ernie model and tokenization. Please note that you need to install the Transformers library before running this code.

```python
import argparse
import torch
from transformers import ErnieForSequenceClassification, ErnieTokenizerFast

class SequenceClassificationPredictor:
    def __init__(self, model_dir, tokenizer_vocab_path, inference_device, runtime_backend, batch_size, sequence_length, logging_interval, fp16, fast_tokenizer):
        self.tokenizer = ErnieTokenizerFast.from_pretrained(tokenizer_vocab_path) if fast_tokenizer else ErnieTokenizerFast.from_pretrained(tokenizer_vocab_path, use_fast=False)
        self.model = ErnieForSequenceClassification.from_pretrained(model_dir)
        self.device = torch.device(inference_device)
        self.runtime = torch.jit.script(self.model.eval().to(self.device))
        self.batch_size = batch_size
        self.sequence_length = sequence_length
        self.logging_interval = logging_interval
        self.fp16 = fp16

    def batchify(self, texts):
        input_ids = self.tokenizer(texts, padding=True, truncation=True, max_length=self.sequence_length, return_tensors="pt", return_attention_mask=True)
        return input_ids.input_ids.to(self.device), input_ids.attention_mask.to(self.device)

    def predict(self, input_ids, attention_mask):
        with torch.cuda.amp.autocast(enabled=self.fp16):
            outputs = self.runtime(input_ids, attention_mask=attention_mask)
        logits = outputs[0]
        logits = logits.detach().cpu().numpy()
        return logits

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_dir', type=str, required=True)
    parser.add_argument('--tokenizer_vocab_path', type=str, required=True)
    parser.add_argument('--inference_device', type=str, default='cuda')
    parser.add_argument('--runtime_backend', type=str, default='torchscript')
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--sequence_length', type=int, default=128)
    parser.add_argument('--logging_interval', type=int, default=10)
    parser.add_argument('--fp16', action='store_true')
    parser.add_argument('--fast_tokenizer', action='store_true')

    args = parser.parse_args()

    predictor = SequenceClassificationPredictor(args.model_dir, args.tokenizer_vocab_path, args.inference_device, args.runtime_backend, args.batch_size, args.sequence_length, args.logging_interval, args.fp16, args.fast_tokenizer)

    texts = ["example 1", "example 2", "example 3", ...]  # Replace with your actual text data
    data_loader = torch.utils.data.DataLoader(texts, batch_size=predictor.batch_size, collate_fn=predictor.batchify)

    for batch_id, batch in enumerate(data_loader):
        input_ids, attention_mask = batch
        logits = predictor.predict(input_ids, attention_mask)
        # Postprocess the logits as needed
        # ...
        for example_id, sentence, label, confidence_score in zip(range(len(texts[batch])), texts[batch], logits[:, 1], logits[:, 2]):
            print(f"Batch ID: {batch_id}, Example ID: {example_id}, Input Sentence: {sentence}, Predicted Label: {label}, Confidence Score: {confidence_score}")

if __name__ == "__main__":
    main()
```

This code creates a command-line interface for parsing the given arguments, initializes the Ernie model for sequence classification, and predicts the output for given texts in batches. The output includes the batch id, example id, input sentences, predicted label, and confidence score.