Enable MLA by default (#1447)

README.md

@@ -225,7 +225,6 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes.
 - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.
 - To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e5m2`.
-- To enable DeepSeek MLA acceleration, add `--enable-mla`.
 - If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](docs/en/custom_chat_template.md).
 - To run tensor parallelism on multiple nodes, add `--nnodes 2`. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-0` be the hostname of the first node and `50000` be an available port.
 ```

docs/en/backend.md

@@ -81,7 +81,6 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 - To enable torch.compile acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes.
 - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.
 - To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e5m2`.
-- To enable DeepSeek MLA acceleration, add `--enable-mla`.
 - If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](docs/en/custom_chat_template.md).
 - To run tensor parallelism on multiple nodes, add `--nnodes 2`. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-0` be the hostname of the first node and `50000` be an available port.
 ```

python/sglang/srt/managers/schedule_batch.py

@@ -40,7 +40,7 @@ global_server_args_dict = {
     "attention_backend": ServerArgs.attention_backend,
     "sampling_backend": ServerArgs.sampling_backend,
     "triton_attention_reduce_in_fp32": ServerArgs.triton_attention_reduce_in_fp32,
-    "enable_mla": ServerArgs.enable_mla,
+    "disable_mla": ServerArgs.disable_mla,
     "torchao_config": ServerArgs.torchao_config,
 }
 

python/sglang/srt/model_executor/model_runner.py

@@ -91,7 +91,7 @@ class ModelRunner:
                 "attention_backend": server_args.attention_backend,
                 "sampling_backend": server_args.sampling_backend,
                 "triton_attention_reduce_in_fp32": server_args.triton_attention_reduce_in_fp32,
-                "enable_mla": server_args.enable_mla,
+                "disable_mla": server_args.disable_mla,
                 "torchao_config": server_args.torchao_config,
             }
         )
@@ -329,7 +329,7 @@ class ModelRunner:
         )
         if (
             self.model_config.attention_arch == AttentionArch.MLA
-            and self.server_args.enable_mla
+            and not self.server_args.disable_mla
         ):
             cell_size = (
                 (self.model_config.kv_lora_rank + self.model_config.qk_rope_head_dim)
@@ -397,7 +397,7 @@ class ModelRunner:
         )
         if (
             self.model_config.attention_arch == AttentionArch.MLA
-            and self.server_args.enable_mla
+            and not self.server_args.disable_mla
         ):
             self.token_to_kv_pool = MLATokenToKVPool(
                 self.max_total_num_tokens,

python/sglang/srt/models/deepseek_v2.py

@@ -507,7 +507,7 @@ class DeepseekV2DecoderLayer(nn.Module):
         rope_theta = getattr(config, "rope_theta", 10000)
         rope_scaling = getattr(config, "rope_scaling", None)
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
-        if global_server_args_dict["enable_mla"]:
+        if not global_server_args_dict["disable_mla"]:
             self.self_attn = DeepseekV2AttentionMLA(
                 config=config,
                 hidden_size=self.hidden_size,
@@ -732,7 +732,7 @@ class DeepseekV2ForCausalLM(nn.Module):
                     )
                     weight_loader(param, loaded_weight)
 
-        if global_server_args_dict["enable_mla"]:
+        if not global_server_args_dict["disable_mla"]:
             for layer_id in range(self.config.num_hidden_layers):
                 self_attn = self.model.layers[layer_id].self_attn
                 w_kc, w_vc = self_attn.kv_b_proj.weight.unflatten(

python/sglang/srt/models/minicpm3.py

@@ -419,7 +419,7 @@ class MiniCPM3DecoderLayer(nn.Module):
         rope_theta = getattr(config, "rope_theta", 10000)
         rope_scaling = getattr(config, "rope_scaling", None)
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
-        if global_server_args_dict["enable_mla"]:
+        if not global_server_args_dict["disable_mla"]:
             self.self_attn = MiniCPM3AttentionMLA(
                 config=config,
                 hidden_size=self.hidden_size,
@@ -653,7 +653,7 @@ class MiniCPM3ForCausalLM(nn.Module):
                     )
                     weight_loader(param, loaded_weight)
 
-        if global_server_args_dict["enable_mla"]:
+        if not global_server_args_dict["disable_mla"]:
             for layer_id in range(self.config.num_hidden_layers):
                 self_attn = self.model.layers[layer_id].self_attn
                 w_kc, w_vc = self_attn.kv_b_proj.weight.unflatten(

python/sglang/srt/server_args.py

@@ -108,12 +108,12 @@ class ServerArgs:
     disable_cuda_graph_padding: bool = False
     disable_disk_cache: bool = False
     disable_custom_all_reduce: bool = False
+    disable_mla: bool = False
     enable_mixed_chunk: bool = False
     enable_torch_compile: bool = False
     max_torch_compile_bs: int = 32
     torchao_config: str = ""
     enable_p2p_check: bool = False
-    enable_mla: bool = False
     triton_attention_reduce_in_fp32: bool = False
 
     # LoRA
@@ -173,7 +173,7 @@ class ServerArgs:
             self.sampling_backend = "pytorch"
 
         # Default kernel backends
-        if self.enable_mla:
+        if not self.disable_mla:
             logger.info("MLA optimization is tunred on. Use triton backend.")
             self.attention_backend = "triton"
 
@@ -514,6 +514,11 @@ class ServerArgs:
             default=False,
             help="Disable the custom all-reduce kernel and fall back to NCCL.",
         )
+        parser.add_argument(
+            "--disable-mla",
+            action="store_true",
+            help="Disable Multi-head Latent Attention (MLA) for DeepSeek-V2.",
+        )
         parser.add_argument(
             "--enable-mixed-chunk",
             action="store_true",
@@ -541,11 +546,6 @@ class ServerArgs:
             action="store_true",
             help="Enable P2P check for GPU access, otherwise the p2p access is allowed by default.",
         )
-        parser.add_argument(
-            "--enable-mla",
-            action="store_true",
-            help="Enable Multi-head Latent Attention (MLA) for DeepSeek-V2.",
-        )
         parser.add_argument(
             "--triton-attention-reduce-in-fp32",
             action="store_true",

test/srt/test_nightly_gsm8k_eval.py

@@ -52,7 +52,7 @@ class TestEvalAccuracyLarge(unittest.TestCase):
         if is_tp2:
             other_args.extend(["--tp", "2"])
         if "DeepSeek" in model:
-            other_args.extend(["--enable-mla", "--mem-frac", "0.85"])
+            other_args.extend(["--mem-frac", "0.85"])
 
         self.process = popen_launch_server(
             model,