Qwen FP8/NVFP4 ModelOPT Quantization support (#7912)

Co-authored-by: Jingyu Xin <jingyux@nvidia.com>

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -517,6 +517,39 @@ class ModelOptFp4Config(QuantizationConfig):
     def get_config_filenames(cls) -> List[str]:
         return ["hf_quant_config.json"]
 
+    @staticmethod
+    def common_group_size(cfg: dict) -> int:
+        """Return the unique group_size across the config; raise if missing/mismatched."""
+        sizes = set()
+
+        # Top-level and 'quantization' block
+        v = cfg.get("group_size")
+        if isinstance(v, int):
+            sizes.add(v)
+        q = cfg.get("quantization")
+        if isinstance(q, dict):
+            v = q.get("group_size")
+            if isinstance(v, int):
+                sizes.add(v)
+
+        # config_groups: accept group-level or nested dicts (e.g., weights/input_activations)
+        for g in (cfg.get("config_groups") or {}).values():
+            if isinstance(g, dict):
+                v = g.get("group_size")
+                if isinstance(v, int):
+                    sizes.add(v)
+                for sub in g.values():
+                    if isinstance(sub, dict):
+                        v = sub.get("group_size")
+                        if isinstance(v, int):
+                            sizes.add(v)
+
+        if not sizes:
+            raise ValueError("No group_size found in config.")
+        if len(sizes) > 1:
+            raise ValueError(f"Inconsistent group_size values: {sorted(sizes)}")
+        return next(iter(sizes))
+
     @classmethod
     def from_config(cls, config: Dict[str, Any]) -> ModelOptFp4Config:
         # Handle two different config formats:
@@ -549,7 +582,7 @@ class ModelOptFp4Config(QuantizationConfig):
                 else:
                     kv_cache_quant_algo = "auto"
 
-            group_size = config.get("group_size")
+            group_size = ModelOptFp4Config.common_group_size(config)
             exclude_modules = config.get("ignore", [])
         else:
             # Fall back to nested format (hf_quant_config.json - legacy format)
@@ -559,7 +592,7 @@ class ModelOptFp4Config(QuantizationConfig):
                 kv_cache_quant_algo = quant_config.get("kv_cache_quant_algo")
                 if not kv_cache_quant_algo:
                     kv_cache_quant_algo = "auto"
-                group_size = quant_config.get("group_size")
+                group_size = ModelOptFp4Config.common_group_size(config)
                 exclude_modules = quant_config.get("exclude_modules", [])
             except (ValueError, KeyError):
                 raise ValueError(

python/sglang/srt/models/qwen3.py

@@ -24,7 +24,10 @@ from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
 from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
 from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
-from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.model_loader.weight_utils import (
+    default_weight_loader,
+    maybe_remap_kv_scale_name,
+)
 from sglang.srt.models.qwen2 import Qwen2MLP as Qwen3MLP
 from sglang.srt.models.qwen2 import Qwen2Model
 from sglang.srt.utils import add_prefix, is_cuda
@@ -458,7 +461,10 @@ class Qwen3ForCausalLM(nn.Module):
                     continue
             if name.startswith("model.vision_tower") and name not in params_dict:
                 continue
-
+            if "scale" in name:
+                name = maybe_remap_kv_scale_name(name, params_dict)
+                if name is None:
+                    continue
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue