[Gemma4] Support quantized MoE (#39045)

Signed-off-by: Dipika Sikka <dipikasikka1@gmail.com>
(cherry picked from commit 3aecdf08)

vllm/model_executor/models/gemma4.py

@@ -1248,21 +1248,27 @@ class Gemma4Model(nn.Module):
             ("gate_up_proj", "up_proj", 1),
         ]
 
-        # MoE expert weight mapping: checkpoint 3D packed tensors are
-        # exploded in _weight_iterator to per-expert 2D weights like:
+        # MoE expert weight mapping: checkpoint can have either:
+        #   1. 3D packed tensors (exploded in _weight_iterator to per-expert 2D)
+        #   2. Already per-expert 2D weights (if quantized)
+        # Map to FusedMoE parameters:
         #   moe.experts.{id}.gate_proj → FusedMoE w1 (shard of w13)
         #   moe.experts.{id}.up_proj   → FusedMoE w3 (shard of w13)
         #   moe.experts.{id}.down_proj → FusedMoE w2
-        # We build the mapping directly since Gemma4 uses bare param
-        # names (no .weight suffix) unlike standard MoE checkpoints.
+        #
+        # Use prefix matching to handle both weights and
+        # quantization scale parameters. The param_name is a prefix ending
+        # in underscore, and weight_name ends with a dot, so that:
+        #   "experts.0.gate_proj.weight_scale" -> "experts.w13_weight_scale"
+        #   "experts.0.gate_proj.weight" -> "experts.w13_weight"
         num_experts = getattr(self.config, "num_experts", None) or 0
         expert_params_mapping = [
             # (param_name, weight_name, expert_id, shard_id)
             (
-                "experts.w13_weight"
+                "experts.w13_"
                 if proj_name in ["gate_proj", "up_proj"]
-                else "experts.w2_weight",
-                f"experts.{expert_id}.{proj_name}",
+                else "experts.w2_",
+                f"experts.{expert_id}.{proj_name}.",
                 expert_id,
                 shard_id,
             )
@@ -1322,9 +1328,21 @@ class Gemma4Model(nn.Module):
                     expert_id,
                     shard_id,
                 ) in expert_params_mapping:
-                    if weight_name not in name:
+                    # Match both:
+                    #  - Bare weights: "experts.0.down_proj" (from 3D explosion)
+                    #  - With suffix: "experts.0.down_proj.weight_scale" (2D quantized)
+                    # weight_name has trailing dot, so check with and without it
+                    weight_name_base = weight_name.rstrip(".")
+                    if weight_name in name:
+                        # Has suffix (e.g., .weight_scale)
+                        moe_name = name.replace(weight_name, param_name)
+                    elif name.endswith(weight_name_base):
+                        # Bare weight (no suffix)
+                        moe_name = name.replace(
+                            weight_name_base, param_name.rstrip("_") + "_weight"
+                        )
+                    else:
                         continue
-                    moe_name = name.replace(weight_name, param_name)
                     if moe_name not in params_dict:
                         continue
                     if is_pp_missing_parameter(moe_name, self):
@@ -1334,15 +1352,12 @@ class Gemma4Model(nn.Module):
                     # orientation for FusedMoE after _weight_iterator:
                     #   gate/up: [I, H] → w1/w3 expects [I, H]
                     #   down:    [H, I] → w2 expects [H, I]
-                    assert loaded_weight.dim() == 2, (
-                        f"Expected 2D expert weight for {weight_name}, "
-                        f"got shape {loaded_weight.shape}"
-                    )
+                    # Scales and other quantization params may be 1D or scalar.
                     weight_loader = param.weight_loader
                     weight_loader(
                         param,
                         loaded_weight,
-                        weight_name + ".weight",
+                        moe_name,  # Pass mapped name (handles both weights and scales)
                         shard_id=shard_id,
                         expert_id=expert_id,
                     )
@@ -1499,6 +1514,11 @@ class Gemma4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, MixtureOfExperts):
                         ".moe.down_proj",
                     )
 
+                # Remap individual 2D expert weights:
+                # .experts.{id}.{proj} → .moe.experts.{id}.{proj}
+                # (This handles per-expert 2D quantized weights)
+                name = re.sub(r"\.experts\.(\d+)\.", r".moe.experts.\1.", name)
+
                 # MoE expert weights: checkpoint stores as 3D packed
                 # tensors.  Explode into per-expert 2D weights for
                 # FusedMoE weight_loader.