[Misc]Add BNB quantization for PaliGemmaForConditionalGeneration

vllm/model_executor/models/paligemma.py

@@ -136,7 +136,18 @@ def forward(self, image_features: torch.Tensor) -> torch.Tensor:
 @INPUT_REGISTRY.register_input_processor(input_processor_for_paligemma)
 class PaliGemmaForConditionalGeneration(nn.Module, SupportsMultiModal,
                                         SupportsPP):
-
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config

vllm/model_executor/models/siglip.py

@@ -344,10 +344,16 @@ def __init__(
 
         self.config = config
         self.activation_fn = get_act_fn(config.hidden_act)
-
-        # For quantization, we require the hidden size to be a multiple of 64
-        quantizable = (config.hidden_size % 64 == 0
-                       and config.intermediate_size % 64 == 0)
+        # Special handling for BNB quantization
+        if quant_config and quant_config.get_name() == "bitsandbytes":
+            quantizable = True
+        else:
+            # For other quantization, we require the hidden size to be a 
+            # multiple of 64
+            quantizable = (
+                config.hidden_size % 64 == 0
+                and config.intermediate_size % 64 == 0
+            )
         self.fc1 = ColumnParallelLinear(
             config.hidden_size,
             config.intermediate_size,