feat: [Qwen3.5] Support block-wise FP8 quantization and model adaptation (#18926)

python/sglang/srt/layers/linear.py

@@ -728,6 +728,51 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
             )
             self.weight_loader_v2(param, loaded_weight_shard, shard_id)
 
+    def _load_merged_block_scale(
+        self, param: BasevLLMParameter, loaded_weight: torch.Tensor
+    ):
+        """
+        Handle block-wise scale loading for MergedColumnParallelLinear.
+        Similar to QKVParallelLinear._load_qkv_block_scale, but for merged column layers.
+        """
+        weight_block_size = self.quant_method.quant_config.weight_block_size
+        block_n, _ = weight_block_size[0], weight_block_size[1]
+        block_n = 1 if getattr(param, "format_ue8m0", False) else block_n
+
+        # Calculate block sizes for each shard
+        shard_block_sizes = []
+        shard_block_offsets = []
+        current_block_offset = 0
+        for output_size in self.output_sizes:
+            shard_block_size = (output_size + block_n - 1) // block_n
+            shard_block_sizes.append(shard_block_size)
+            shard_block_offsets.append(current_block_offset)
+            current_block_offset += shard_block_size
+
+        # Load each shard
+        for shard_id, (shard_block_offset, shard_block_size) in enumerate(
+            zip(shard_block_offsets, shard_block_sizes)
+        ):
+            # Extract the shard from loaded_weight
+            loaded_weight_shard = loaded_weight.narrow(
+                param.output_dim, shard_block_offset, shard_block_size
+            )
+
+            # Calculate per-rank offset and size (considering TP)
+            rank_shard_offset = shard_block_offset // self.tp_size
+            rank_shard_size = shard_block_size // self.tp_size
+
+            # Load into the parameter
+            param.load_merged_column_weight(
+                loaded_weight=loaded_weight_shard,
+                shard_id=shard_id,
+                shard_offset=rank_shard_offset,
+                shard_size=rank_shard_size,
+                tp_rank=self.tp_rank,
+                tp_size=self.tp_size,
+                use_presharded_weights=self.use_presharded_weights,
+            )
+
     def weight_loader_v2(
         self,
         param: BasevLLMParameter,
@@ -743,6 +788,9 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                     tp_size=self.tp_size,
                 )
                 return
+            elif isinstance(param, BlockQuantScaleParameter):
+                self._load_merged_block_scale(param, loaded_weight)
+                return
             elif type(param) in (RowvLLMParameter, BasevLLMParameter):
                 param.load_merged_column_weight(
                     loaded_weight=loaded_weight,