chore: vendor sglang v0.5.10 snapshot

third_party/sglang/docs/diffusion/performance/index.md

@@ -0,0 +1,42 @@
+# Performance
+
+This section covers the main performance levers for SGLang Diffusion: attention backends, caching acceleration, and profiling.
+
+## Overview
+
+| Optimization | Type | Description |
+|--------------|------|-------------|
+| **Cache-DiT** | Caching | Block-level caching with DBCache, TaylorSeer, and SCM |
+| **TeaCache** | Caching | Timestep-level caching based on temporal similarity |
+| **Attention Backends** | Kernel | Optimized attention implementations (FlashAttention, SageAttention, etc.) |
+| **Profiling** | Diagnostics | PyTorch Profiler and Nsight Systems guidance |
+
+## Start Here
+
+- Use [Attention Backends](attention_backends.md) to choose the best backend for your model and hardware.
+- Use [Caching Acceleration](cache/index.md) to reduce denoising cost with Cache-DiT or TeaCache.
+- Use [Profiling](profiling.md) when you need to diagnose a bottleneck rather than guess.
+
+## Caching at a Glance
+
+- [Cache-DiT](cache/cache_dit.md) is block-level caching for diffusers pipelines and higher speedup-oriented tuning.
+- [TeaCache](cache/teacache.md) is timestep-level caching built into SGLang model families.
+
+```{toctree}
+:maxdepth: 1
+
+attention_backends
+cache/index
+profiling
+```
+
+## Current Baseline Snapshot
+
+For Ring SP benchmark details, see:
+
+- [Ring SP Performance](ring_sp_performance.md)
+
+## References
+
+- [Cache-DiT Repository](https://github.com/vipshop/cache-dit)
+- [TeaCache Paper](https://arxiv.org/abs/2411.14324)