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Revert "Replace enable_flashinfer_mla argument with attention_backend" (#5048)

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Lianmin Zheng
2025-04-03 13:30:56 -07:00
committed by GitHub
parent b8b6008f47
commit 74885a848b
8 changed files with 20 additions and 21 deletions
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6
docs/backend/server_arguments.md
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@@ -138,7 +138,7 @@ Please consult the documentation below to learn more about the parameters you ma
## Kernel backend
* `attention_backend`: The backend for attention computation and KV cache management, and can be one of `fa3`, `flashinfer`, `triton` or `torch_native`. When deploying deepseek models, this argument is for specifying the MLA backend it uses.
* `attention_backend`: The backend for attention computation and KV cache management.
* `sampling_backend`: The backend for sampling.
## Constrained Decoding
@@ -192,5 +192,5 @@ Please consult the documentation below to learn more about the parameters you ma
* `cuda_graph_bs`: The batch sizes to capture by `CudaGraphRunner`. By default this is done for you.
* `torchao_config`: Experimental feature that optimizes the model with [torchao](https://github.com/pytorch/ao). Possible choices are: int8dq, int8wo, int4wo-<group_size>, fp8wo, fp8dq-per_tensor, fp8dq-per_row.
* `triton_attention_num_kv_splits`: Use to adjust the number of KV splits in triton kernels. Default is 8.
* `enable_flashinfer_mla`: Use the attention backend with flashinfer MLA wrapper for deepseek models. **This argument will be deprecated soon! Please use `--attention_backend flashinfer` instead for switching on flashfiner mla!**
* `flashinfer_mla_disable_ragged`: Disable usage of ragged prefill wrapper for flashinfer mla attention backend. Should be used when flashinfer is used as mla backend turned on.
* `enable_flashinfer_mla`: Use the attention backend with flashinfer MLA wrapper for deepseek models. When providing this argument, `attention_backend` argument is overridden.
* `flashinfer_mla_disable_ragged`: Disable usage of ragged prefill wrapper for flashinfer mla attention backend. Should be used when `enable_flashinfer_mla` is turned on.

4
docs/references/deepseek.md
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@@ -86,7 +86,7 @@ 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, [Flashinfer](https://docs.flashinfer.ai/api/mla.html) and Triton backends. It can be set with `--attention-backend` argument.
- **Flashinfer MLA Wrapper**: By providing `--enable-flashinfer-mla` argument, the server will use MLA kernels customized by Flashinfer. More details can be referred to [this document](https://docs.flashinfer.ai/api/mla.html). Under long input scenarios, flashinfer mla can improve performance significantly. Optimized triton kernels will be used when flashinfer mla is turned off.
- **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.
@@ -149,7 +149,7 @@ python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 --spec
```
- The draft model are available at huggingface: [lmsys/DeepSeek-V3-0324-NextN](https://huggingface.co/lmsys/DeepSeek-V3-0324-NextN), [lmsys/DeepSeek-R1-NextN](https://huggingface.co/lmsys/DeepSeek-R1-NextN). It can also be exported from original DeepSeek-V3/R1 model with [export_deepseek_nextn.py](https://github.com/sgl-project/sglang/blob/main/scripts/export_deepseek_nextn.py) script.
- The best configuratin 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.
- Currently when using flashinfer mla wrapper (`--attention-backend flashinfer`) and speculative decoding together, the `--speculative-eagle-topk` parameter should be set to `1`. The MTP feature on FlashAttention 3 backend is still under beta.
- Currently when using flashinfer mla wrapper (`--enable-flashinfer-mla`) and speculative decoding together, the `--speculative-eagle-topk` parameter should be set to `1`.
- 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.