gemma3: impl `get_attention_sliding_window_size` for attn init (#4823)

python/sglang/srt/models/gemma3_causal.py

@@ -47,6 +47,12 @@ from sglang.srt.model_loader.weight_utils import (
 from sglang.srt.utils import add_prefix, make_layers
 
 
+# Aligned with HF's implementation, using sliding window inclusive with the last token
+# SGLang assumes exclusive
+def get_attention_sliding_window_size(config):
+    return config.sliding_window - 1
+
+
 # Adapted from:
 # https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/gemma3.py
 def extract_layer_index(prefix: str) -> int:
@@ -170,7 +176,7 @@ class Gemma3Attention(nn.Module):
             self.rope_scaling = {"rope_type": "default"}
             # FIXME(mick): idk why vllm does this
             # self.sliding_window = config.interleaved_sliding_window
-            self.sliding_window = config.sliding_window
+            self.sliding_window = get_attention_sliding_window_size(config)
         else:
             # Global attention. Use the values in config.json.
             self.rope_theta = config.rope_theta
@@ -184,6 +190,8 @@ class Gemma3Attention(nn.Module):
             num_kv_heads=self.num_kv_heads,
             layer_id=layer_id,
             logit_cap=getattr(self.config, "attn_logit_softcapping", None),
+            # Module must also define `get_attention_sliding_window_size` to correctly initialize
+            # attention backend in `ForwardBatch`.
             sliding_window_size=self.sliding_window,
             prefix=add_prefix("attn", prefix),
         )
@@ -609,6 +617,9 @@ class Gemma3ForCausalLM(PreTrainedModel):
     def get_input_embeddings(self) -> nn.Embedding:
         return self.model.embed_tokens
 
+    def get_attention_sliding_window_size(self):
+        return get_attention_sliding_window_size(self.config)
+
     def dtype(self) -> torch.dtype:
         return next(self.parameters()).dtype
 
@@ -621,7 +632,6 @@ class Gemma3ForCausalLM(PreTrainedModel):
         input_embeds: torch.Tensor = None,
         **kwargs,
     ) -> LogitsProcessor:
-
         hidden_states = self.model(
             input_ids, positions, forward_batch, input_embeds, **kwargs
         )

python/sglang/srt/models/gemma3_mm.py

@@ -268,6 +268,12 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
     def get_input_embeddings(self) -> nn.Embedding:
         return self.language_model.get_input_embeddings()
 
+    def get_attention_sliding_window_size(self):
+        """
+        This value is used to initialize attention backends in `ForwardBatch`.
+        """
+        return self.language_model.get_attention_sliding_window_size()
+
     def get_image_feature(self, image_input: MultimodalInputs):
         """
         Projects the last hidden state from the vision model into language model space.