[Model] Adding Qwen3 and Qwen3MoE (sgl-project#4693)

Author: yhyang201

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

@@ -100,8 +100,11 @@ def __init__(
             self.num_wrappers = 1
             self.dispatch_reason = None
 
-        # Qwen2 models require higher flashinfer workspace size
-        if "Qwen2ForCausalLM" in model_runner.model_config.hf_config.architectures:
+        # Qwen2/Qwen3 models require higher flashinfer workspace size
+        if (
+            "Qwen2ForCausalLM" in model_runner.model_config.hf_config.architectures
+            or "Qwen3ForCausalLM" in model_runner.model_config.hf_config.architectures
+        ):
             global_config.flashinfer_workspace_size = 512 * 1024 * 1024
 
         # Allocate buffers

python/sglang/srt/models/qwen2.py

@@ -239,6 +239,7 @@ def __init__(
         config: Qwen2Config,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        decoder_layer_type: type[nn.Module] = Qwen2DecoderLayer,
     ) -> None:
         super().__init__()
         self.config = config
@@ -250,9 +251,11 @@ def __init__(
             quant_config=quant_config,
             prefix=add_prefix("embed_tokens", prefix),
         )
+        # Use the provided decoder layer type or default to Qwen2DecoderLayer
+        decoder_layer_type = decoder_layer_type or Qwen2DecoderLayer
         self.layers = make_layers(
             config.num_hidden_layers,
-            lambda idx, prefix: Qwen2DecoderLayer(
+            lambda idx, prefix: decoder_layer_type(
                 layer_id=idx,
                 config=config,
                 quant_config=quant_config,

python/sglang/srt/models/qwen2_moe.py

@@ -47,7 +47,7 @@
 from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
-from sglang.srt.utils import add_prefix
+from sglang.srt.utils import add_prefix, make_layers
 
 expert_distribution_recorder = ExpertDistributionRecorder()
 
@@ -334,6 +334,7 @@ def __init__(
         config: PretrainedConfig,
         quant_config: Optional[QuantizationConfig] = None,
         prefix: str = "",
+        decoder_layer_type: type[nn.Module] = Qwen2MoeDecoderLayer,
     ) -> None:
         super().__init__()
         self.padding_idx = config.pad_token_id
@@ -344,16 +345,17 @@ def __init__(
             config.hidden_size,
             prefix=add_prefix("embed_tokens", prefix),
         )
-        self.layers = nn.ModuleList(
-            [
-                Qwen2MoeDecoderLayer(
-                    config,
-                    layer_id,
-                    quant_config=quant_config,
-                    prefix=add_prefix(f"layers.{layer_id}", prefix),
-                )
-                for layer_id in range(config.num_hidden_layers)
-            ]
+        # Use the provided decoder layer type or default to Qwen2MoeDecoderLayer
+        decoder_layer_type = decoder_layer_type or Qwen2MoeDecoderLayer
+        self.layers = make_layers(
+            config.num_hidden_layers,
+            lambda idx, prefix: decoder_layer_type(
+                layer_id=idx,
+                config=config,
+                quant_config=quant_config,
+                prefix=prefix,
+            ),
+            prefix=add_prefix("layers", prefix),
         )
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)