[Model] Modify MolmoForCausalLM MLP (vllm-project#11510)

Signed-off-by: Jee Jee Li <[email protected]>

vllm/model_executor/models/molmo.py

@@ -464,24 +464,27 @@ def forward(
 class MolmoMLP(nn.Module):
     """Molmo's LLM mlp."""
 
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        input_dim: Optional[int] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-    ) -> None:
+    def __init__(self,
+                 config: PretrainedConfig,
+                 input_dim: Optional[int] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 proj_name: str = "gate_up_proj") -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
         self.intermediate_size = config.intermediate_size // 2
 
-        # Feed-forward input projection.
-        self.gate_up_proj = MergedColumnParallelLinear(
-            input_dim or self.hidden_size,
-            [self.intermediate_size] * 2,
-            bias=False,
-            quant_config=quant_config,
-        )
-
+        # Molmo's LLM proj weights are already merged into the disk, while
+        # image_projector proj is separate. If the same proj_name were used, it
+        # would create ambiguity and make it difficult to support BNB and LoRA.
+        self.proj_name = proj_name
+        setattr(
+            self, proj_name,
+            MergedColumnParallelLinear(
+                input_dim or self.hidden_size,
+                [self.intermediate_size] * 2,
+                bias=False,
+                quant_config=quant_config,
+            ))
         # Activation function.
         self.act_fn = SiluAndMul()
 
@@ -497,7 +500,7 @@ def forward(
         self,
         x: torch.Tensor,
     ) -> torch.Tensor:
-        gate_up, _ = self.gate_up_proj(x)
+        gate_up, _ = getattr(self, self.proj_name)(x)
         x = self.act_fn(gate_up)
         x, _ = self.down_proj(x)
         return x
@@ -520,7 +523,9 @@ def __init__(
                                         prefix=f"{prefix}.self_attn")
 
         # MLP block.
-        self.mlp = MolmoMLP(config, quant_config=quant_config)
+        self.mlp = MolmoMLP(config,
+                            quant_config=quant_config,
+                            proj_name="gate_up_proj")
 
         # LayerNorm
         assert config.layer_norm_type == "rms"
@@ -616,6 +621,7 @@ def __init__(
             config,
             input_dim=vision_config.image_emb_dim,
             quant_config=quant_config,
+            proj_name="merged_linear",
         )
 
         image_dim = vision_config.image_emb_dim * len(self.vit_layers)
@@ -714,8 +720,8 @@ def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
+            ("merged_linear", "gate_proj", 0),
+            ("merged_linear", "up_proj", 1),
         ]
         params_dict = dict(self.named_parameters())
         loaded_params: Set[str] = set()