[Bugfix] Allow fallback to AWQ from AWQMarlin at per-layer granularity (#13119)

vllm/model_executor/layers/linear.py

@@ -290,29 +290,30 @@ class ColumnParallelLinear(LinearBase):
                  quant_config: Optional[QuantizationConfig] = None,
                  output_sizes: Optional[list[int]] = None,
                  prefix: str = ""):
-        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
-                         quant_config, prefix)
-
-        self.gather_output = gather_output
-
         # Divide the weight matrix along the last dimension.
-        tp_size = get_tensor_model_parallel_world_size()
-        assert self.quant_method is not None
-        self.output_size_per_partition = divide(self.output_size, tp_size)
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.input_size_per_partition = input_size
+        self.output_size_per_partition = divide(output_size, self.tp_size)
         self.output_partition_sizes = [self.output_size_per_partition]
         # If QKV or MergedColumn, use output size of each partition.
         if hasattr(self, "output_sizes"):
             self.output_partition_sizes = [
-                divide(output_size, tp_size)
+                divide(output_size, self.tp_size)
                 for output_size in self.output_sizes
             ]
 
+        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
+                         quant_config, prefix)
+
+        self.gather_output = gather_output
+
         if output_sizes is None:
             output_sizes = [output_size]
 
+        assert self.quant_method is not None
         self.quant_method.create_weights(
             layer=self,
-            input_size_per_partition=self.input_size,
+            input_size_per_partition=self.input_size_per_partition,
             output_partition_sizes=self.output_partition_sizes,
             input_size=self.input_size,
             output_size=self.output_size,
@@ -1044,22 +1045,24 @@ class RowParallelLinear(LinearBase):
                  reduce_results: bool = True,
                  quant_config: Optional[QuantizationConfig] = None,
                  prefix: str = ""):
+        # Divide the weight matrix along the first dimension.
+        self.tp_rank = get_tensor_model_parallel_rank()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.input_size_per_partition = divide(input_size, self.tp_size)
+        self.output_size_per_partition = output_size
+        self.output_partition_sizes = [output_size]
+
         super().__init__(input_size, output_size, skip_bias_add, params_dtype,
                          quant_config, prefix)
 
         self.input_is_parallel = input_is_parallel
         self.reduce_results = reduce_results
 
-        # Divide the weight matrix along the last dimension.
-        self.tp_rank = get_tensor_model_parallel_rank()
-        self.tp_size = get_tensor_model_parallel_world_size()
-        self.input_size_per_partition = divide(input_size, self.tp_size)
         assert self.quant_method is not None
-
         self.quant_method.create_weights(
             layer=self,
             input_size_per_partition=self.input_size_per_partition,
-            output_partition_sizes=[self.output_size],
+            output_partition_sizes=self.output_partition_sizes,
             input_size=self.input_size,
             output_size=self.output_size,
             params_dtype=self.params_dtype,

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -13,15 +13,17 @@ from vllm.model_executor.layers.fused_moe.layer import (
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod,
                                                set_weight_attrs)
-from vllm.model_executor.layers.quantization.awq import is_layer_skipped_awq
+from vllm.model_executor.layers.quantization.awq import (AWQConfig,
+                                                         is_layer_skipped_awq)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.utils import replace_parameter
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     apply_awq_marlin_linear, awq_to_marlin_zero_points, check_marlin_supported,
-    marlin_make_empty_g_idx, marlin_make_workspace, marlin_moe_permute_scales,
-    marlin_permute_scales, moe_awq_to_marlin_zero_points,
-    verify_marlin_supported, verify_marlin_supports_shape)
+    check_marlin_supports_layer, marlin_make_empty_g_idx,
+    marlin_make_workspace, marlin_moe_permute_scales, marlin_permute_scales,
+    moe_awq_to_marlin_zero_points, verify_marlin_supported,
+    verify_marlin_supports_shape)
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.parameter import (GroupQuantScaleParameter,
                                            PackedvLLMParameter)
@@ -40,18 +42,17 @@ class AWQMarlinConfig(QuantizationConfig):
         8: scalar_types.uint8,
     }
 
-    def __init__(self,
-                 weight_bits: int,
-                 group_size: int,
-                 zero_point: bool,
+    def __init__(self, weight_bits: int, group_size: int, zero_point: bool,
                  lm_head_quantized: bool,
-                 modules_to_not_convert: Optional[List[str]] = None) -> None:
+                 modules_to_not_convert: Optional[List[str]],
+                 full_config: Dict[str, Any]) -> None:
         self.pack_factor = 32 // weight_bits  # packed into int32
         self.group_size = group_size
         self.zero_point = zero_point
         self.lm_head_quantized = lm_head_quantized
         self.weight_bits = weight_bits
         self.modules_to_not_convert = modules_to_not_convert or []
+        self.full_config = full_config
 
         if self.weight_bits not in self.TYPE_MAP:
             raise ValueError(f"Unsupported num_bits = {self.weight_bits}. "
@@ -96,7 +97,7 @@ class AWQMarlinConfig(QuantizationConfig):
         modules_to_not_convert = cls.get_from_keys_or(
             config, ["modules_to_not_convert"], None)
         return cls(weight_bits, group_size, zero_point, lm_head_quantized,
-                   modules_to_not_convert)
+                   modules_to_not_convert, config)
 
     @classmethod
     def override_quantization_method(cls, hf_quant_cfg,
@@ -124,6 +125,13 @@ class AWQMarlinConfig(QuantizationConfig):
             (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
             if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
                 return UnquantizedLinearMethod()
+            # Check if the layer is supported by AWQMarlin.
+            if not check_marlin_supports_layer(layer, self.group_size):
+                logger.warning_once(
+                    f"Layer '{prefix}' is not supported by AWQMarlin. "
+                    "Falling back to unoptimized AWQ kernels.")
+                return AWQConfig.from_config(
+                    self.full_config).get_quant_method(layer, prefix)
             return AWQMarlinLinearMethod(self)
         elif isinstance(layer, FusedMoE):
             return AWQMoEMethod(self)

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -16,6 +16,8 @@ from vllm.model_executor.layers.quantization.base_config import (
 from vllm.model_executor.layers.quantization.gptq import GPTQConfig
 from vllm.model_executor.layers.quantization.gptq_marlin import (
     GPTQMarlinConfig)
+from vllm.model_executor.layers.quantization.utils.marlin_utils import (
+    check_marlin_supports_layer)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 
@@ -87,8 +89,8 @@ class MoeWNA16Config(QuantizationConfig):
             modules_to_not_convert = []
         elif linear_quant_method == "awq":
             has_zp = cls.get_from_keys(config, ["zero_point"])
-            modules_to_not_convert = cls.get_from_keys(
-                config, ["modules_to_not_convert"])
+            modules_to_not_convert = cls.get_from_keys_or(
+                config, ["modules_to_not_convert"], None)
         else:
             raise ValueError("moe_wna16 only support gptq and awq.")
 
@@ -135,7 +137,8 @@ class MoeWNA16Config(QuantizationConfig):
                     return GPTQConfig.from_config(
                         self.full_config).get_quant_method(layer, prefix)
             elif self.linear_quant_method == "awq":
-                if self.use_marlin:
+                if self.use_marlin and check_marlin_supports_layer(
+                        layer, self.group_size):
                     return AWQMarlinConfig.from_config(
                         self.full_config).get_quant_method(layer, prefix)
                 else:

vllm/model_executor/layers/quantization/utils/marlin_utils.py

@@ -6,6 +6,7 @@ import numpy
 import torch
 
 from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase
 from vllm.platforms import current_platform
 from vllm.scalar_type import ScalarType, scalar_types
 
@@ -135,6 +136,20 @@ def check_marlin_supports_shape(output_size_per_partition: int,
     return True, None
 
 
+def check_marlin_supports_layer(layer: LinearBase, group_size: int) \
+                                    -> bool:
+    output_size_per_partition = getattr(layer, "output_size_per_partition",
+                                        None) or layer.output_size
+    input_size_per_partition = getattr(layer, "input_size_per_partition",
+                                       None) or layer.input_size
+
+    return check_marlin_supports_shape(
+        output_size_per_partition=output_size_per_partition,
+        input_size_per_partition=input_size_per_partition,
+        input_size=layer.input_size,
+        group_size=group_size)[0]
+
+
 def marlin_make_workspace(output_size_per_partition: int,
                           device: torch.device) -> torch.Tensor:
     max_workspace_size = (output_size_per_partition //