Update doc for MLA attention backends (sgl-project#6034)

Author: Fridge003

docs/backend/server_arguments.md

@@ -168,7 +168,7 @@ Please consult the documentation below and [server_args.py](https://github.com/s
 
 | Arguments | Description | Defaults |
 |----------|-------------|---------|
-| `attention_backend` | This argument specifies the backend for attention computation and KV cache management, which can be `fa3`, `flashinfer`, `triton`, `cutlass_mla`, or `torch_native`. When deploying DeepSeek models, use this argument to specify the MLA backend. | None |
+| `attention_backend` | This argument specifies the backend for attention computation and KV cache management, which can be `fa3`, `flashinfer`, `triton`, `flashmla`, `cutlass_mla`, or `torch_native`. When deploying DeepSeek models, use this argument to specify the MLA backend. | None |
 | `sampling_backend` | Specifies the backend used for sampling. | None |
 
 ## Constrained Decoding

docs/references/deepseek.md

@@ -87,7 +87,7 @@ Please refer to [the example](https://github.com/sgl-project/sglang/tree/main/be
 
 - **Weight Absorption**: By applying the associative law of matrix multiplication to reorder computation steps, this method balances computation and memory access and improves efficiency in the decoding phase.
 
-- **MLA Attention Backends**: Currently SGLang supports different optimized MLA attention backends, including [FlashAttention3](https://github.com/Dao-AILab/flash-attention), [Flashinfer](https://docs.flashinfer.ai/api/mla.html), and [Triton](https://github.com/triton-lang/triton) backends. The default FA3 provides good performance across wide workloads.
+- **MLA Attention Backends**: Currently SGLang supports different optimized MLA attention backends, including [FlashAttention3](https://github.com/Dao-AILab/flash-attention), [Flashinfer](https://docs.flashinfer.ai/api/mla.html), [FlashMLA](https://github.com/deepseek-ai/FlashMLA), [CutlassMLA](https://github.com/sgl-project/sglang/pull/5390), and [Triton](https://github.com/triton-lang/triton) backends. The default FA3 provides good performance across wide workloads.
 
 - **FP8 Quantization**: W8A8 FP8 and KV Cache FP8 quantization enables efficient FP8 inference. Additionally, we have implemented Batched Matrix Multiplication (BMM) operator to facilitate FP8 inference in MLA with weight absorption.
 
@@ -158,7 +158,7 @@ Add arguments `--speculative-algorithm`, `--speculative-num-steps`, `--speculati
 python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 --speculative-algorithm EAGLE --speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2 --trust-remote-code --tp 8
 ```
 - The best configuration for `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` can be searched with [bench_speculative.py](https://github.com/sgl-project/sglang/blob/main/scripts/playground/bench_speculative.py) script for given batch size. The minimum configuration is `--speculative-num-steps 1 --speculative-eagle-topk 1 --speculative-num-draft-tokens 2`, which can achieve speedup for larger batch sizes.
-When using FlashInfer MLA wrapper (`--attention-backend flashinfer`) with speculative decoding, set the `--speculative-eagle-topk` parameter to `1`. The FlashAttention 3 backend also only supports `--speculative-eagle-topk 1`.
+- FlashAttention3 and Triton backend fully supports MTP usage. For FlashInfer backend (`--attention-backend flashinfer`) with speculative decoding,`--speculative-eagle-topk` parameter should be set to `1`. MTP support for the FlashMLA backend and CutlassMLA backend is still under development.
 - To enable DeepSeek MTP for large batch sizes (>32), there are some parameters should be changed (Reference [this discussion](https://github.com/sgl-project/sglang/issues/4543#issuecomment-2737413756)):
   - Adjust `--max-running-requests` to a larger number. The default value is `32` for MTP. For larger batch sizes, you should increase this value beyond the default value.
   - Set `--cuda-graph-bs`. It's a list of batch sizes for cuda graph capture. The default captured batch sizes for speculative decoding is set [here](https://github.com/sgl-project/sglang/blob/49420741746c8f3e80e0eb17e7d012bfaf25793a/python/sglang/srt/model_executor/cuda_graph_runner.py#L126). You can include more batch sizes into it.