[Fix] model init / XPU / transformers-v5 / bench-image fixes (#28292)

Co-authored-by: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>

python/sglang/benchmark/datasets/image.py

@@ -115,7 +115,10 @@ def create_mm_data_row(
         # Note (Xinyuan): This is a workaround for an issue where some tokenizers do not support content as a list. (e.g. InternVL)
         print(f"Error applying chat template: {e}, fallback to <image> tag")
         # Some tokenizers do not support list content; fall back to a placeholder in the text
-        prompt_str = f"<image>{text_prompt}"
+        if type(processor).__name__ == "MiniCPMOProcessor":
+            prompt_str = f"(<image>./</image>){text_prompt}"
+        else:
+            prompt_str = f"<image>{text_prompt}"
 
     # Calculate total tokens (text + vision)
     if type(processor).__name__ == "KimiK25Processor":
@@ -125,6 +128,12 @@ def create_mm_data_row(
             medias=medias,
             return_tensors="pt",
         )["input_ids"].numel()
+    elif type(processor).__name__ == "VLChatProcessor":
+        prompt_len = processor(
+            prompt=prompt_str,
+            images=images,
+            force_batchify=False,
+        )["input_ids"].numel()
     elif type(processor).__name__ == "DeepseekVLV2Processor":
         result = processor(
             conversations=prompt_str,

python/sglang/srt/managers/mm_utils.py

@@ -1227,7 +1227,7 @@ def tensor_hash(tensor_list) -> int:
         # CPU path: hash each tensor incrementally without concat
         hasher = hashlib.sha256()
         for t in tensors:
-            t = t.detach().contiguous()
+            t = t.detach().cpu().contiguous()
             hasher.update(memoryview(t.reshape(-1).view(torch.uint8).numpy()))
         hash_bytes = hasher.digest()[:8]
         return int.from_bytes(hash_bytes, byteorder="big", signed=False)
@@ -1235,7 +1235,7 @@ def tensor_hash(tensor_list) -> int:
     # Single tensor
     if tensor.is_cuda:
         return gpu_tensor_hash(tensor.cuda())
-    tensor = tensor.detach().contiguous()
+    tensor = tensor.detach().cpu().contiguous()
     hasher = hashlib.sha256()
     hasher.update(memoryview(tensor.reshape(-1).view(torch.uint8).numpy()))
     hash_bytes = hasher.digest()[:8]

python/sglang/srt/models/afmoe.py

@@ -45,7 +45,7 @@ from sglang.srt.layers.linear import (
 )
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
-from sglang.srt.layers.moe.moe_runner.triton_utils import fused_moe
+from sglang.srt.layers.moe.moe_runner.triton_utils.fused_moe import fused_moe
 from sglang.srt.layers.moe.topk import TopK
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.radix_attention import RadixAttention

python/sglang/srt/models/baichuan.py

@@ -48,10 +48,11 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.runtime_context import get_parallel
-from sglang.srt.utils import add_prefix, is_npu
+from sglang.srt.utils import add_prefix, is_npu, is_xpu
 from sglang.srt.utils.hf_transformers_utils import get_rope_config
 
 _is_npu = is_npu()
+_is_xpu = is_xpu()
 
 
 def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
@@ -190,7 +191,9 @@ class BaiChuanAttention(nn.Module):
             alibi_slopes = _get_alibi_slopes(self.total_num_heads)
             alibi_slopes = alibi_slopes[head_start:head_end]
             self.alibi_slopes = torch.tensor(
-                alibi_slopes, dtype=dtype, device="npu" if _is_npu else "cuda"
+                alibi_slopes,
+                dtype=dtype,
+                device="npu" if _is_npu else "xpu" if _is_xpu else "cuda",
             )
         else:
             self.rotary_emb = get_rope(

python/sglang/srt/models/gemma3_causal.py

@@ -577,9 +577,8 @@ class Gemma3TextModel(PreTrainedModel):
 
         global_config = copy.deepcopy(config)
         global_config.rope_parameters = {
+            **rope_params["full_attention"],
             "rope_theta": global_theta,
-            "factor": config.rope_parameters["full_attention"]["factor"],
-            "rope_type": "linear",
         }
         self.rotary_emb = Gemma3RotaryEmbedding(config=global_config)
         self.gradient_checkpointing = False

python/sglang/srt/models/gemma3n_causal.py

@@ -389,16 +389,21 @@ class Gemma3nAttention(nn.Module):
                 self.head_dim,
                 rotary_dim=self.head_dim,
                 max_position=config.max_position_embeddings,
-                base=config.rope_local_base_freq,
+                base=config.rope_parameters.get("sliding_attention", {}).get(
+                    "rope_theta", 10000.0
+                ),
                 rope_scaling={"rope_type": "default"},
             )
         else:
+            full_attn_rope = config.rope_parameters.get("full_attention", {})
             self.rotary_emb = get_rope(
                 self.head_dim,
                 rotary_dim=self.head_dim,
                 max_position=config.max_position_embeddings,
-                base=config.rope_parameters["rope_theta"],
+                base=full_attn_rope.get("rope_theta", 1000000.0),
-                rope_scaling=config.rope_parameters,
+                rope_scaling=(
+                    full_attn_rope if full_attn_rope else {"rope_type": "default"}
+                ),
             )
 
         self.sliding_window = config.sliding_window if self.is_sliding else None

python/sglang/srt/models/gemma3n_mm.py

@@ -445,8 +445,8 @@ class Gemma3nForConditionalGeneration(PreTrainedModel):
             input_ids, hidden_states, self.language_model.embed_tokens, forward_batch
         )
 
-    def tie_weights(self):
+    def tie_weights(self, **kwargs):
-        return self.language_model.tie_weights()
+        return self.language_model.tie_weights(**kwargs)
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [

python/sglang/srt/models/lightonocr.py

@@ -82,11 +82,12 @@ class LightOnOCRForConditionalGeneration(nn.Module):
 
         # Build VisionEncoderArgs from config
         vision_config = config.vision_config
+        config_dict = vision_config.to_dict()
+        if config_dict.get("rope_parameters"):
+            config_dict["rope_theta"] = config_dict["rope_parameters"].get("rope_theta")
         dataclass_fields = {field.name for field in fields(VisionEncoderArgs)}
         vision_args = {
-            key: value
+            key: value for key, value in config_dict.items() if key in dataclass_fields
-            for key, value in vision_config.to_dict().items()
-            if key in dataclass_fields
         }
         # LightOnOCR stores these at the top-level config
         if "image_token_id" not in vision_args:

python/sglang/srt/models/phi3_small.py

@@ -388,7 +388,7 @@ class Phi3SmallForCausalLM(nn.Module):
             quant_config=quant_config,
             prefix=add_prefix("lm_head", prefix),
         )
-        if self.config.tie_word_embeddings:
+        if getattr(self.config, "tie_word_embeddings", True):
             self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config)
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
@@ -466,7 +466,10 @@ class Phi3SmallForCausalLM(nn.Module):
                 continue
             if name.endswith(".bias") and name not in params_dict:
                 continue
-            if self.config.tie_word_embeddings and "lm_head.weight" in name:
+            if (
+                getattr(self.config, "tie_word_embeddings", True)
+                and "lm_head.weight" in name
+            ):
                 continue
 
             param = params_dict[name]

python/sglang/srt/models/transformers.py

@@ -118,7 +118,9 @@ def _getattr_first(obj, names, default=None):
 
 
 def _resolve_attention_backend_model_cls(config: PretrainedConfig):
-    model_cls = getattr(transformers, getattr(config, "architectures", [""])[0], None)
+    model_cls = getattr(
+        transformers, (getattr(config, "architectures", None) or [""])[0], None
+    )
     if model_cls is not None:
         return model_cls