[v1] use async streamer

[hiyouga]

What does this PR do?

Fixes # (issue)

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• Did you write any new necessary tests?

[gemini-code-assist]

Summary of Changes

Hello @hiyouga, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on modernizing the streaming capabilities within the v1 module by transitioning to an asynchronous text streamer, which is crucial for non-blocking I/O operations in large language model inference. Concurrently, it introduces several refactorings across core components, including the BaseTrainer and kernel application logic, to improve code structure, type consistency, and overall maintainability. These changes aim to streamline the model's operational flow and standardize internal API interactions.

Highlights

• Asynchronous Streaming Implementation: The text generation process has been upgraded to use AsyncTextIteratorStreamer from the transformers library, replacing the synchronous TextIteratorStreamer. This change enables native asynchronous handling of text streams, improving efficiency and responsiveness in base_sampler.py.
• Kernel Application API Refinement: The apply_default_kernels function in interface.py has been refactored to accept explicit model and include_kernels arguments with type hints, moving away from **kwargs. This enhances code clarity, maintainability, and type safety for kernel application.
• BaseTrainer Refactoring: The BaseTrainer class in base_trainer.py has been updated to use a Renderer object instead of a generic Processor in its constructor. Additionally, some internal methods (init_model_and_optimizer and create_dataloader) have been renamed for better organization and to indicate their private nature.

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[gemini-code-assist]

Code Review

This pull request primarily refactors the streaming implementation to use AsyncTextIteratorStreamer, which is a significant improvement for handling asynchronous generation. The changes also include several beneficial refactorings across the codebase, such as making function signatures more explicit and adopting modern Python type hints, which enhance code clarity and maintainability. Overall, the changes are well-implemented. I have one suggestion to improve a type hint for consistency and correctness.