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

docs/backend/server_arguments.md

@@ -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.

docs/references/deepseek.md

@@ -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.

python/sglang/srt/layers/attention/flashinfer_mla_backend.py

@@ -71,6 +71,8 @@ class FlashInferMLAAttnBackend(AttentionBackend):
         self.device = model_runner.device
         self.skip_prefill = skip_prefill
 
+        global_config.enable_flashinfer_mla = True
+
         # Allocate buffers
         global global_workspace_buffer
         if global_workspace_buffer is None:

python/sglang/srt/managers/schedule_batch.py

@@ -76,6 +76,7 @@ global_server_args_dict = {
     "device": ServerArgs.device,
     "speculative_accept_threshold_single": ServerArgs.speculative_accept_threshold_single,
     "speculative_accept_threshold_acc": ServerArgs.speculative_accept_threshold_acc,
+    "enable_flashinfer_mla": ServerArgs.enable_flashinfer_mla,
     "enable_flashmla": ServerArgs.enable_flashmla,
     "disable_radix_cache": ServerArgs.disable_radix_cache,
     "flashinfer_mla_disable_ragged": ServerArgs.flashinfer_mla_disable_ragged,
@@ -1434,7 +1435,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
 
         # Create seq_lens_cpu when needed
         if (
-            global_server_args_dict["attention_backend"] == "flashinfer_mla"
+            global_server_args_dict["enable_flashinfer_mla"]
             or global_server_args_dict["enable_flashmla"]
             or global_server_args_dict["attention_backend"] == "fa3"
         ):

python/sglang/srt/model_executor/model_runner.py

@@ -151,6 +151,7 @@ class ModelRunner:
                 "device": server_args.device,
                 "speculative_accept_threshold_single": server_args.speculative_accept_threshold_single,
                 "speculative_accept_threshold_acc": server_args.speculative_accept_threshold_acc,
+                "enable_flashinfer_mla": server_args.enable_flashinfer_mla,
                 "enable_flashmla": server_args.enable_flashmla,
                 "disable_radix_cache": server_args.disable_radix_cache,
                 "flashinfer_mla_disable_ragged": server_args.flashinfer_mla_disable_ragged,
@@ -222,14 +223,10 @@ class ModelRunner:
         ):
             # TODO: add MLA optimization on CPU
             if server_args.device != "cpu":
-                if (
-                    server_args.attention_backend == "flashinfer"
-                    or server_args.enable_flashinfer_mla
-                ):
+                if server_args.enable_flashinfer_mla:
                     logger.info(
-                        "MLA optimization is turned on. Use flashinfer backend."
+                        "MLA optimization is turned on. Use flashinfer mla backend."
                     )
-                    # Here we use a special flashinfer_mla tag to differentiate it from normal flashinfer backend
                     server_args.attention_backend = "flashinfer_mla"
                 elif server_args.enable_flashmla:
                     logger.info("MLA optimization is turned on. Use flashmla decode.")

python/sglang/srt/models/deepseek_v2.py

@@ -684,6 +684,7 @@ class DeepseekV2AttentionMLA(nn.Module):
         self.w_vc = None
         self.w_scale = None
 
+        self.enable_flashinfer_mla = global_server_args_dict["enable_flashinfer_mla"]
         self.flashinfer_mla_disable_ragged = global_server_args_dict[
             "flashinfer_mla_disable_ragged"
         ]
@@ -691,7 +692,7 @@ class DeepseekV2AttentionMLA(nn.Module):
         self.rocm_fused_decode_mla = os.getenv("SGLANG_ROCM_FUSED_DECODE_MLA") == "1"
 
     def no_absorb(self, forward_batch: ForwardBatch) -> bool:
-        if self.attention_backend == "flashinfer_mla":
+        if self.enable_flashinfer_mla:
             # Flashinfer MLA: Do not absorb when enabling ragged prefill
             return (
                 not self.flashinfer_mla_disable_ragged

python/sglang/srt/server_args.py

@@ -179,7 +179,7 @@ class ServerArgs:
     tool_call_parser: Optional[str] = None
     enable_hierarchical_cache: bool = False
     hicache_ratio: float = 2.0
-    enable_flashinfer_mla: bool = False  # TODO: remove this argument
+    enable_flashinfer_mla: bool = False
     enable_flashmla: bool = False
     flashinfer_mla_disable_ragged: bool = False
     warmups: Optional[str] = None
@@ -836,7 +836,7 @@ class ServerArgs:
         parser.add_argument(
             "--enable-flashinfer-mla",
             action="store_true",
-            help="Enable FlashInfer MLA optimization. This argument will be deprecated soon! Please use '--attention-backend flashinfer' instead for switching on flashfiner mla!",
+            help="Enable FlashInfer MLA optimization",
         )
         parser.add_argument(
             "--enable-flashmla",

test/srt/test_mla_flashinfer.py

@@ -26,8 +26,7 @@ class TestFlashinferMLA(CustomTestCase):
                     "--enable-torch-compile",
                     "--cuda-graph-max-bs",
                     "2",
-                    "--attention-backend",
-                    "flashinfer",
+                    "--enable-flashinfer-mla",
                 ]
             )
         cls.process = popen_launch_server(
@@ -70,8 +69,8 @@ class TestFlashinferMLANoRagged(CustomTestCase):
                     "--disable-cuda-graph",
                     "--cuda-graph-max-bs",
                     "4",
-                    "--attention-backend",
-                    "flashinfer",
+                    "--enable-flashinfer-mla",
+                    "--flashinfer-mla-disable-ragged",
                 ]
             )
         cls.process = popen_launch_server(
@@ -126,8 +125,7 @@ class TestFlashinferMLAMTP(CustomTestCase):
                     "1",
                     "--speculative-num-draft-tokens",
                     "4",
-                    "--attention-backend",
-                    "flashinfer",
+                    "--enable-flashinfer-mla",
                 ]
             )
         cls.process = popen_launch_server(