Principle:InternLM Lmdeploy Pipeline Initialization
| Knowledge Sources | |
|---|---|
| Domains | LLM_Inference, API_Design |
| Last Updated | 2026-02-07 15:00 GMT |
Overview
A factory pattern that creates a ready-to-use inference pipeline by automatically detecting model architecture, selecting the optimal backend, and initializing the async engine.
Description
Pipeline Initialization encapsulates the complex startup sequence of an LLM inference engine behind a single factory function call. The process involves:
- Model resolution: Downloading from HuggingFace Hub or validating a local path
- Architecture detection: Reading model config to determine architecture family
- Backend selection: Automatically choosing TurboMind or PyTorch based on model support
- VLM detection: Identifying vision-language models and enabling multimodal processing
- Engine startup: Launching async engine with event loop thread, KV cache allocation, and weight loading
- Template configuration: Loading the appropriate chat template for the model
This abstraction allows users to go from a model identifier to a working inference engine in a single function call.
Usage
Use this when starting any inference workload, whether batch offline processing, interactive chat, or as the backbone for an API server. The factory function is the primary entry point for all LMDeploy Python usage.
Theoretical Basis
The pipeline initialization follows the Abstract Factory pattern combined with Strategy pattern for backend selection:
Pseudo-code:
# Abstract initialization flow
def initialize_pipeline(model_path, config):
model_config = download_and_read_config(model_path)
backend = auto_select_backend(model_config, config)
is_vlm = detect_vision_language_model(model_config)
if is_vlm:
engine = VLAsyncEngine(model_path, backend, config)
else:
engine = AsyncEngine(model_path, backend, config)
template = load_chat_template(model_config)
return Pipeline(engine, template)