[Misc]Add BNB quantization for MolmoForCausalLM (#11551)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

vllm/model_executor/model_loader/loader.py

@@ -11,7 +11,8 @@ import os
 import warnings
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
-from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple, cast
+from typing import (Any, Callable, Dict, Generator, Iterable, List, Optional,
+                    Tuple, cast)
 
 import gguf
 import huggingface_hub
@@ -706,6 +707,8 @@ class BitsAndBytesModelLoader(BaseModelLoader):
         # Store all module names (from transformers) that support
         # BNB quantization.
         self.target_modules: List[str] = []
+        # mapping weight names from transformers to vllm.
+        self.weight_mapper: Callable = lambda name: name
 
     def _get_weight_files(
         self,
@@ -763,9 +766,12 @@ class BitsAndBytesModelLoader(BaseModelLoader):
 
     def _hf_weight_iter(self, hf_weights_files, use_safetensors: bool):
         if use_safetensors:
-            return safetensors_weights_iterator(hf_weights_files)
+            iterator = safetensors_weights_iterator(hf_weights_files)
         else:
-            return pt_weights_iterator(hf_weights_files)
+            iterator = pt_weights_iterator(hf_weights_files)
+        for name, param in iterator:
+            # mapping weight names from transformers to vllm.
+            yield self.weight_mapper(name), param
 
     def _get_quantized_weights_iterator(
         self,
@@ -782,12 +788,12 @@ class BitsAndBytesModelLoader(BaseModelLoader):
         try:
             import bitsandbytes
 
-            if bitsandbytes.__version__ < "0.44.0":
+            if bitsandbytes.__version__ < "0.45.0":
                 raise ImportError("bitsandbytes version is wrong. Please "
-                                  "install bitsandbytes>=0.44.0.")
+                                  "install bitsandbytes>=0.45.0.")
         except ImportError as err:
-            raise ImportError("Please install bitsandbytes>=0.44.0 via "
-                              "`pip install bitsandbytes>=0.44.0` to use "
+            raise ImportError("Please install bitsandbytes>=0.45.0 via "
+                              "`pip install bitsandbytes>=0.45.0` to use "
                               "bitsandbytes quantizer.") from err
 
         hf_weights_files, use_safetensors = self._prepare_weights(
@@ -991,7 +997,7 @@ class BitsAndBytesModelLoader(BaseModelLoader):
             if isinstance(module, (LinearBase, )):
                 last_name = name.split(".")[-1]
                 if sub_modules := inverse_stacked_mapping.get(last_name, []):
-                    # Map vllm's names to transformers' names.
+                    # Map vllm's names to transformers's names.
                     for sub_name in sub_modules:
                         self.target_modules.append(
                             name.replace(last_name, sub_name))
@@ -1013,6 +1019,10 @@ class BitsAndBytesModelLoader(BaseModelLoader):
                 f"Model {type(model).__name__} does not support BitsAndBytes "
                 "quantization yet.")
 
+        # For some models like Molmo, we need to use hf_to_vllm_mapper
+        # to ensure correct loading of weights.
+        if hf_to_vllm_mapper := getattr(model, "hf_to_vllm_mapper", None):
+            self.weight_mapper = lambda name: hf_to_vllm_mapper._map_name(name)
         # Modules whose weights might have fused on disk
         # we need their output_sizes to make shard in flight correctly with TP
         self.maybe_fused_weights_modules: Dict[str, List[int]] = {}

vllm/model_executor/models/molmo.py

@@ -461,30 +461,71 @@ class MolmoAttention(nn.Module):
         return output
 
 
-class MolmoMLP(nn.Module):
+class SwiGLU(nn.Module):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, gate = x.chunk(2, dim=-1)
+        # Note that the order is reversed compared to
+        # SiluAndMul.
+        return x * F.silu(gate)
+
+
+class LanuageModelMLP(nn.Module):
     """Molmo's LLM mlp."""
 
     def __init__(self,
+                 config: PretrainedConfig,
+                 input_dim: Optional[int] = None,
+                 quant_config: Optional[QuantizationConfig] = None) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size // 2
+
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_dim or self.hidden_size,
+            [self.intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+        )
+        # Activation function.
+        self.act_fn = SwiGLU()
+        # Feed-forward output projection.
+        self.down_proj = RowParallelLinear(
+            self.intermediate_size,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class ImageProjectorMLP(nn.Module):
+    """Molmo's image_projector mlp."""
+
+    def __init__(
+        self,
         config: PretrainedConfig,
         input_dim: Optional[int] = None,
         quant_config: Optional[QuantizationConfig] = None,
-                 proj_name: str = "gate_up_proj") -> None:
+    ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
         self.intermediate_size = config.intermediate_size // 2
 
-        # 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(
+        self.merged_linear = MergedColumnParallelLinear(
             input_dim or self.hidden_size,
             [self.intermediate_size] * 2,
             bias=False,
             quant_config=quant_config,
-            ))
+        )
         # Activation function.
         self.act_fn = SiluAndMul()
 
@@ -500,7 +541,7 @@ class MolmoMLP(nn.Module):
         self,
         x: torch.Tensor,
     ) -> torch.Tensor:
-        gate_up, _ = getattr(self, self.proj_name)(x)
+        gate_up, _ = self.merged_linear(x)
         x = self.act_fn(gate_up)
         x, _ = self.down_proj(x)
         return x
@@ -523,9 +564,7 @@ class MolmoDecoderLayer(nn.Module):
                                         prefix=f"{prefix}.self_attn")
 
         # MLP block.
-        self.mlp = MolmoMLP(config,
-                            quant_config=quant_config,
-                            proj_name="gate_up_proj")
+        self.mlp = LanuageModelMLP(config, quant_config=quant_config)
 
         # LayerNorm
         assert config.layer_norm_type == "rms"
@@ -617,11 +656,10 @@ class MolmoVisionBackbone(nn.Module):
             vision_config,
             nlayers=len(self.vit_layers),
             quant_config=quant_config)
-        self.image_projector = MolmoMLP(
+        self.image_projector = ImageProjectorMLP(
             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)
@@ -842,10 +880,6 @@ class MolmoModel(nn.Module):
         loaded_params: Set[str] = set()
 
         for name, loaded_weight in weights:
-            if "gate_up_proj" in name:
-                up_proj, gate_proj = loaded_weight.chunk(2, dim=0)
-                loaded_weight = torch.cat([gate_proj, up_proj], dim=0)
-
             if name.endswith(".bias") and name not in params_dict:
                 continue
             if is_pp_missing_parameter(name, self):
@@ -1157,6 +1191,12 @@ class MolmoForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):
         },
     )
 
+    # BitandBytes specific attributes
+    bitsandbytes_stacked_params_mapping = {
+        "gate_proj": ("merged_linear", 0),
+        "up_proj": ("merged_linear", 1),
+    }
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config