[Bugfix]: Fix glm46 awq marlin moe wna16 compatibility (#30210)

Signed-off-by: baonudesifeizhai <baonudesifeizhai@gmail.com>

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -895,6 +895,48 @@ def get_moe_configs(
     return None
 
 
+def _ensure_block_size_k_divisible(
+    size_k: int, block_size_k: int, group_size: int
+) -> int:
+    """Ensure block_size_k is a divisor of size_k and divisible by group_size.
+
+    This ensures BLOCK_SIZE_K compatibility with MoeWNA16 CUDA kernel which
+    requires size_k % BLOCK_SIZE_K == 0 and BLOCK_SIZE_K % group_size == 0.
+
+    Args:
+        size_k: The size_k dimension that must be divisible by result.
+        block_size_k: Preferred block size (will be adjusted if needed).
+        group_size: The result must be divisible by this.
+
+    Returns:
+        A valid BLOCK_SIZE_K that divides size_k and is divisible by group_size.
+    """
+    # Fast path: already valid
+    if size_k % block_size_k == 0 and block_size_k % group_size == 0:
+        return block_size_k
+
+    # Find the largest value that:
+    # 1. Divides size_k (size_k % candidate == 0)
+    # 2. Is divisible by group_size (candidate % group_size == 0)
+    # 3. Is <= block_size_k (prefer smaller values close to block_size_k)
+    #
+    # Strategy: Search from min(block_size_k, size_k) down to group_size,
+    # stepping by group_size to ensure divisibility by group_size
+    max_search = min(block_size_k, size_k)
+    start = (max_search // group_size) * group_size
+    for candidate in range(start, group_size - 1, -group_size):
+        if size_k % candidate == 0:
+            return candidate
+
+    # Fallback: if group_size divides size_k, use it
+    # This should always be true with correct group_size configuration
+    if size_k % group_size == 0:
+        return group_size
+
+    # This should not happen with correct group_size, but ensure divisibility
+    return size_k
+
+
 def get_moe_wna16_block_config(
     config: dict[str, int],
     use_moe_wna16_cuda: bool,
@@ -960,6 +1002,9 @@ def get_moe_wna16_block_config(
             # at the same time.
             block_size_n = 1024
 
+        # Ensure BLOCK_SIZE_K is a divisor of size_k for CUDA kernel compatibility
+        block_size_k = _ensure_block_size_k_divisible(size_k, block_size_k, group_size)
+
         return {"BLOCK_SIZE_N": block_size_n, "BLOCK_SIZE_K": block_size_k}
 
 

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -60,7 +60,7 @@ class MoeWNA16Config(QuantizationConfig):
 
         if self.linear_quant_method == "gptq":
             self.use_marlin = GPTQMarlinConfig.is_gptq_marlin_compatible(full_config)
-        elif self.linear_quant_method == "awq":
+        elif self.linear_quant_method in ("awq", "awq_marlin"):
             capability_tuple = current_platform.get_device_capability()
             device_capability = (
                 -1 if capability_tuple is None else capability_tuple.to_int()
@@ -107,7 +107,7 @@ class MoeWNA16Config(QuantizationConfig):
         if linear_quant_method == "gptq":
             has_zp = not cls.get_from_keys(config, ["sym"])
             modules_to_not_convert = []
-        elif linear_quant_method == "awq":
+        elif linear_quant_method in ("awq", "awq_marlin"):
             has_zp = cls.get_from_keys(config, ["zero_point"])
             modules_to_not_convert = cls.get_from_keys_or(
                 config, ["modules_to_not_convert"], None
@@ -184,7 +184,7 @@ class MoeWNA16Config(QuantizationConfig):
                     return GPTQConfig.from_config(self.full_config).get_quant_method(
                         layer, prefix
                     )
-            elif self.linear_quant_method == "awq":
+            elif self.linear_quant_method in ("awq", "awq_marlin"):
                 if self.use_marlin and check_marlin_supports_layer(
                     layer, self.group_size
                 ):
@@ -468,7 +468,8 @@ class MoeWNA16Method(FusedMoEMethodBase):
             shard_size = layer.intermediate_size_per_partition
 
             # convert gptq and awq weight to a standard format
-            if layer.quant_config.linear_quant_method == "awq":
+            # awq_marlin uses the same weight format as awq
+            if layer.quant_config.linear_quant_method in ("awq", "awq_marlin"):
                 assert layer.quant_config.weight_bits == 4
                 if "weight" in weight_name:
                     loaded_weight = convert_awq_tensor(loaded_weight, "qweight")