[feat]: simplify sglang installation with submodule, auto-sync CI, and version alignment

- Add kvcache-ai/sglang as git submodule at third_party/sglang (branch = main)
- Add top-level install.sh for one-click source installation (sglang + kt-kernel)
- Add sglang-kt as hard dependency in kt-kernel/pyproject.toml
- Add CI workflow to auto-sync sglang submodule daily and create PR
- Add CI workflow to build and publish sglang-kt to PyPI
- Integrate sglang-kt build into release-pypi.yml (version.py bump publishes both packages)
- Align sglang-kt version with ktransformers via SGLANG_KT_VERSION env var injection
- Update Dockerfile to use submodule and inject aligned version
- Update all 13 doc files, CLI hints, and i18n strings to reference new install methods

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

doc/en/kt-kernel/deepseek-v3.2-sglang-tutorial.md

@@ -30,7 +30,7 @@ This tutorial demonstrates how to run DeepSeek V3.2 model inference using SGLang
 Before starting, ensure you have:
 
 1. **KT-Kernel installed** - Follow the [installation guide](./kt-kernel_intro.md#installation)
-2. **SGLang installed** - Follow [SGLang integration steps](./kt-kernel_intro.md#integration-with-sglang)
+2. **SGLang installed** - Install the kvcache-ai fork: `pip install sglang-kt` or run `./install.sh` from the ktransformers root
 3. **CUDA toolkit** - Compatible with your GPU (CUDA 11.8+ recommended)
 4. **Hugging Face CLI** - For downloading models:
    ```bash